Search

Medical image segmentation through the use of deep learning is becoming a trend targeting to automate disease detection and provide effective treatment. Traditionally huge manual efforts are associated with medical image processing by medical staff. The wide use of neural networks driven by the progressive advancement in computing power and the availability of training data provides instrumental means to automate medical image processing. A polyp refers to an abnormal growth that can occur in various parts of the body. While the majority of polyps are noncancerous or benign, there are instances where certain types can develop into cancer. Detecting and segmenting polyps is highly valuable for identifying early signs and potentially enabling more effective treatment of colon cancer. In this paper, we introduce a novel method for segmenting polyp images. The proposed method utilizes convolutional neural networks (CNNs) and employs an enhanced attention mechanism. To cater to a more granular attention view, this paper enhances the Convolutional Block Attention Module by introducing the multi‐focal channel attention (MFCA) concept, which we call MFCA. In the proposed MFCA channel attention, focal attention spots allow us to consider scattered areas of interest more effectively. To evaluate the effectiveness of the proposed method, multiple experiments are conducted on five well‐known benchmark datasets for polyp image segmentation: Kvasir, CVC‐Clinic DB, CVC‐Colon DB, CVC‐T, and ETIS‐Larib. Various testing scenarios are examined, including the impact of two focal attention spots and four focal attention spots. The experimental results demonstrated that the proposed method achieved superior performance compared to previous state‐of‐the‐art techniques on two of the benchmark datasets, and ranked second on a third dataset. [ABSTRACT FROM AUTHOR]

Skin diseases and lesions can be ambiguous to recognize due to the similarity of lesions and enhanced imaging features. In this study, we compared three cutting-edge deep learning frameworks for dermoscopic segmentation: U-Net, SegAN, and MultiResUNet. We used a dermoscopic dataset including detailed lesion annotations with segmentation masks to help train and evaluate models on the precise localization of melanomas. SegAN is a special type of Generative Adversarial Network (GAN) that introduces a new architecture by adding generator and discriminator steps. U-Net has become a common strategy in segmentation to encode and decode image features for limited data. MultiResUNet is a U-Net-based architecture that overcomes the insufficient data problem in medical imaging by extracting contextual details. We trained the three frameworks on colored images after preprocessing. We added incremental Gaussian noise to measure the robustness of segmentation performance. We evaluated the frameworks using the following parameters: accuracy, sensitivity, specificity, Dice and Jaccard coefficients. Our accuracy results show that SegAN (92%) and MultiResUNet (92%) both outperform U-Net (86%), which is a well-known segmentation framework for skin lesion analysis. MultiResUNet sensitivity (96%) outperforms the methods in the challenge leaderboard. These results suggest that SegAN and MultiResUNet are more resistant techniques against noise in dermoscopic segmentation. [ABSTRACT FROM AUTHOR]

Introduction: Lung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment. Methods: This paper explores the lung CT image segmentation method by generative adversarial networks. We employ a variety of generative adversarial networks and used their capability of image translation to perform image segmentation. The generative adversarial network is employed to translate the original lung image into the segmented image. Results: The generative adversarial networks-based segmentation method is tested on real lung image data set. Experimental results show that the proposed method outperforms the state-of-the-art method. Discussion: The generative adversarial networks-based method is effective for lung image segmentation. [ABSTRACT FROM AUTHOR]

Doctors typically use non‐contrast‐enhanced computed tomography (NCECT) in the treatment of kidney cancer to map kidney and tumour structural information to functional imaging single‐photon emission computed tomography, which is then used to assess patient kidney function and predict postoperative recovery. However, the assessment of kidney function and formulation of surgical plans is constrained by the low contrast of tumours in NCECT, which hinders the acquisition of accurate tumour boundaries. Therefore, this study designed a radiomics feature‐enhanced level‐set evolution (RFLSE) to precisely segment small‐sample low‐contrast kidney tumours. Integration of high‐dimensional radiomics features into the level‐set energy function enhances the edge detection capability of low‐contrast kidney tumours. The use of sensitive radiomics features to control the regional term parameters achieves adaptive adjustment of the curve evolution amplitude, improving the level‐set segmentation process. The experimental data used low‐contrast, limited‐sample tumours provided by hospitals, as well as the public datasets BUSI18 and KiTS19. Comparative results with advanced energy functionals and deep learning models demonstrate the precision and robustness of RFLSE segmentation. Additionally, the application value of RFLSE in assisting doctors with accurately marking tumours and generating high‐quality pseudo‐labels for deep learning datasets is demonstrated. [ABSTRACT FROM AUTHOR]

Optic disc (OD) and optic cup (OC) segmentation is an important task in ophthalmic medicine and is crucial for aiding glaucoma screening. With the development of smart healthcare and the increase of large datasets, there is an increasing number of research efforts targeting OD and OC segmentation, making it particularly important to provide a systematic review of the latest advances in the field. This paper presents a systematic review of commonly used datasets, evaluation metrics, and related research results in the field of OD and OC segmentation. The advantages and disadvantages of segmentation techniques based on traditional and deep learning methods are comparatively analysed. In addition, this study emphasizes the importance of OD and OC segmentation efforts in smart healthcare. Despite the technological advances, the lack of generalization capability is still a major obstacle limiting its clinical application. To address this issue, this study explores unsupervised domain adaptation methods to enhance the generalization performance of segmentation techniques and provide new strategies for clinical diagnosis. Finally, this paper discusses the challenges and future research directions faced by OD and OC segmentation when applied in the medical field to help readers comprehensively grasp the research dynamics in this area. [ABSTRACT FROM AUTHOR]

Edge detection is an essential image processing act that is crucial for many computer vision applications such as object detection, image segmentation, face recognition, text recognition, medical imaging, and autonomous vehicles. Deep learning is the most advanced and widely used tool of them all. In this paper, we present a novel deep learning model and use image datasets to test it. Our model uses a fractional calculus tool, which could enhance gradient approaches' performances. Specifically, we approximate the fractional-order derivative-order neural network (GLFNet) using a Grünwald–Letnikov fractional definition. First, the original dataset is subjected to a Grünwald–Letnikov fractional order. After that, the CNN model is updated with the new dataset, concluding the standard CNN procedure. The training rate, the improvement in the F-measure for identifying the effective edge while maintaining the CNN model's memory consumption, and the values of the loss errors between the prediction and training processes were all tested using the MNIST dataset. Our experiments show that GLFNet considerably enhances edge detection. GLFNet outperformed CNN with an average loss error ratio of 15.40, suggesting fewer loss mistakes. The F1-measure ratio of 0.81 indicates that GLFNet can compete with CNN in terms of precision and recall. The training time for GLFNet was lowered by an average ratio of 1.14 when compared to CNN, while inference time was faster with a ratio of 1.14, indicating increased efficiency. These findings demonstrate the efficacy of introducing Grünwald–Letnikov fractional convolution into deep learning models, resulting in more precise and reliable edge detection while preserving comparable memory utilization. [ABSTRACT FROM AUTHOR]

The accurate distribution of joints on the tunnel face is crucial for assessing the stability and safety of surrounding rock during tunnel construction. This paper introduces the Mask R-CNN image segmentation algorithm, a state-of-the-art deep learning model, to achieve efficient and accurate identification and extraction of joints on tunnel face images. First, digital images of tunnel faces were captured and stitched, resulting in 286 complete images suitable for analysis. Then, the joints on the tunnel face were extracted using traditional image processing algorithms, the commonly used U-net image segmentation model, and the Mask R-CNN image segmentation model introduced in this paper to address the lack of recognition accuracy. Finally, the extraction results obtained by the three methods were compared. The comparison results show that the joint extraction method based on the Mask R-CNN image segmentation deep learning model introduced in this paper achieved the best joint extraction effect with a Dice similarity coefficient of 87.48%, outperforming traditional methods and the U-net model, which scored 60.59% and 75.36%, respectively, realizing accurate and efficient acquisition of tunnel face rock joints. These findings suggest that the Mask R-CNN model can be effectively implemented in real-time monitoring systems for tunnel construction projects. [ABSTRACT FROM AUTHOR]

The characterization of beached and marine microplastic debris is critical to understanding how plastic litter accumulates across the world's oceans and identifying hotspots that should be targeted for early cleanup efforts. Currently, the most common monitoring method to quantify microplastics at sea requires physical sampling using surface trawling and sifting for beached microplastics, which are then followed by manual counting and laboratory analysis. The need for manual counting is time-consuming, operatordependent, and incurs high costs, thereby preventing scalable deployment of consistent marine plastic monitoring worldwide. Here, we describe a workflow combining a simple experimental setup with advanced image processing techniques to conduct both quantitative and qualitative assessments of microplastic (0.05 cm < particle size <0.5 cm). The image processing relies on deep learning models designed for image segmentation and classification. The results demonstrated comparable or superior performance in comparison to manual identification for microplastic particles with a 96% accuracy. Thus, the use of the model offers an efficient, more robust, standardized, highly replicable, and less labor-intensive alternative to particle counting. In addition to the relative simplicity of the network architecture used that made it easy to train, the model presents promising prospects for better-standardized reporting of plastic particles surveyed in the environment. We also made the models and datasets open-source and created a user-friendly web interface for directly annotating new images. [ABSTRACT FROM AUTHOR]

In clinical conditions limited by equipment, attaining lightweight skin lesion segmentation is pivotal as it facilitates the integration of the model into diverse medical devices, thereby enhancing operational efficiency. However, the lightweight design of the model may face accuracy degradation, especially when dealing with complex images such as skin lesion images with irregular regions, blurred boundaries, and oversized boundaries. To address these challenges, we propose an efficient lightweight attention network (ELANet) for the skin lesion segmentation task. In ELANet, two different attention mechanisms of the bilateral residual module (BRM) can achieve complementary information, which enhances the sensitivity to features in spatial and channel dimensions, respectively, and then multiple BRMs are stacked for efficient feature extraction of the input information. In addition, the network acquires global information and improves segmentation accuracy by putting feature maps of different scales through multi-scale attention fusion (MAF) operations. Finally, we evaluate the performance of ELANet on three publicly available datasets, ISIC2016, ISIC2017, and ISIC2018, and the experimental results show that our algorithm can achieve 89.87%, 81.85%, and 82.87% of the mIoU on the three datasets with a parametric of 0.459 M, which is an excellent balance between accuracy and lightness and is superior to many existing segmentation methods. [ABSTRACT FROM AUTHOR]

In remote sensing image processing, the segmentation of clouds and their shadows is a fundamental and vital task. For cloud images, traditional deep learning methods often have weak generalization capabilities and are prone to interference from ground objects and noise, which not only results in poor boundary segmentation but also causes false and missed detections of small targets. To address these issues, we proposed a multi-branch attention fusion network (MAFNet). In the encoder section, the dual branches of ResNet50 and the Swin transformer extract features together. A multi-branch attention fusion module (MAFM) uses positional encoding to add position information. Additionally, multi-branch aggregation attention (MAA) in the MAFM fully fuses the same level of deep features extracted by ResNet50 and the Swin transformer, which enhances the boundary segmentation ability and small target detection capability. To address the challenge of detecting small cloud and shadow targets, an information deep aggregation module (IDAM) was introduced to perform multi-scale deep feature aggregation, which supplements high semantic information, improving small target detection. For the problem of rough segmentation boundaries, a recovery guided module (RGM) was designed in the decoder section, which enables the model to effectively allocate attention to complex boundary information, enhancing the network's focus on boundary information. Experimental results on the Cloud and Cloud Shadow dataset, HRC-WHU dataset, and SPARCS dataset indicate that MAFNet surpasses existing advanced semantic segmentation techniques. [ABSTRACT FROM AUTHOR]

Autonomous navigation is the leading technology in current era, in this intelligent traffic light, sign detection, ADAS and obstacle detections were playing major role. Image segmentation is the process of dividing an image into different regions, or semantic classes. This is a challenging problem in autonomous vehicle technology because it requires the vehicle to be able to understand its surroundings to safely navigate. The major challenges in this platform are the accuracy and efficiency of model performance. The proposed method in the abstract uses a convolutional neural network (CNN) to perform image segmentation. CNNs are a type of deep learning model that is well-suited for image processing tasks. The CNN in this paper was trained on a local city dataset, and it was able to achieve a mean intersection over union (IoU) of 73%. IoU is a measure of how well the segmentation results match the ground truth labels. A score of 100% indicates that the segmentation is perfect, while a score of 0% indicates that the segmentation is completely wrong. This means that the method can segment images at a very fast rate, which is important for autonomous vehicles that need to make real-time decisions. Overall, the proposed method is a promising approach for image segmentation in autonomous vehicles. It can achieve high accuracy and speed, and it is easy to implement using Python. The proposed method attains an accuracy of 98.34 %, a Sensitivity of 97.26 % and a sensitivity of 96.37 % had been attained. The method could be used to improve the safety and efficiency of autonomous vehicles by enabling them to better understand their surroundings. [ABSTRACT FROM AUTHOR]

Many advanced super-resolution reconstruction methods have been proposed recently, but they often require high computational and memory resources, making them incompatible with low-power devices in reality. To address this problem, we propose a simple yet efficient super-resolution reconstruction method using waveform representation and multi-layer perceptron (MLP) for image processing. Firstly, we partition the original image and its down-sampled version into multiple patches and introduce WaveBlock to process these patches. WaveBlock represents patches as waveform functions with amplitude and phase and extracts representative feature representations by dynamically adjusting phase terms between tokens and fixed weights. Next, we fuse the extracted features through a feature fusion block and finally reconstruct the image using sub-pixel convolution. Extensive experimental results demonstrate that SRWave-MLP performs excellently in both quantitative evaluation metrics and visual quality while having significantly fewer parameters than state-of-the-art efficient super-resolution methods. [ABSTRACT FROM AUTHOR]

With the rapid advancement of modern hardware technology, breakthroughs have been made in many areas of artificial intelligence research, leading to the direction of machine replacement or assistance in various fields. However, most artificial intelligence or deep learning techniques require large amounts of training data and are typically applicable to a single task objective. Acquiring such large training datasets can be particularly challenging, especially in domains like medical imaging. In the field of image processing, few-shot image segmentation is an area of active research. Recent studies have employed deep learning and meta-learning approaches to enable models to segment objects in images with only a small amount of training data, allowing them to quickly adapt to new task objectives. This paper proposes a network architecture for meta-learning few-shot image segmentation, utilizing a meta-learning classification weight transfer network to generate masks for few-shot image segmentation. The architecture leverages pre-trained classification weight transfers to generate informative prior masks and employs pre-trained feature extraction architecture for feature extraction of query and support images. Furthermore, it utilizes a Feature Enrichment Module to adaptively propagate information from finer features to coarser features in a top-down manner for query image feature extraction. Finally, a classification module is employed for query image segmentation prediction. Experimental results demonstrate that compared to the baseline using the mean Intersection over Union (mIOU) as the evaluation metric, the accuracy increases by 1.7% in the one-shot experiment and by 2.6% in the five-shot experiment. Thus, compared to the baseline, the proposed architecture with meta-learning classification weight transfer network for mask generation exhibits superior performance in few-shot image segmentation. [ABSTRACT FROM AUTHOR]

Image segmentation and identification are crucial to modern medical image processing techniques. This research provides a novel and effective method for identifying and segmenting liver tumors from public CT images. Our approach leverages the hybrid ResUNet model, a combination of both the ResNet and UNet models developed by the Monai and PyTorch frameworks. The ResNet deep dense network architecture is implemented on public CT scans using the MSD Task03 Liver dataset. The novelty of our method lies in several key aspects. First, we introduce innovative enhancements to the ResUNet architecture, optimizing its performance, especially for liver tumor segmentation tasks. Additionally, by harassing the capabilities of Monai, we streamline the implementation process, eliminating the need for manual script writing and enabling faster, more efficient model development and optimization. The process of preparing images for analysis by a deep neural network involves several steps: data augmentation, a Hounsfield windowing unit, and image normalization. ResUNet network performance is measured by using the DC metric Dice coefficient. This approach, which utilizes residual connections, has proven to be more reliable than other existing techniques. This approach achieved DC values of 0.98% for detecting liver tumors and 0.87% for segmentation. Both qualitative and quantitative evaluations show promising results regarding model precision and accuracy. The implications of this research are that it could be used to increase the precision and accuracy of liver tumor detection and liver segmentation, reflecting the potential of the proposed method. This could help in the early diagnosis and treatment of liver cancer, which can ultimately improve patient prognosis. [ABSTRACT FROM AUTHOR]

Reconstruction of 2-dimensional transcerebellar images into 3-dimensional transcerebellar has an important role in diagnosis and treatment planning in neurology. In this study, we propose the 3D-FHNet method to produce an accurate three-dimensional representation of transcerebellar images. The process begins with the selection of images and the application of image augmentation and enhancement techniques to improve the quality of the initial image. Furthermore, segmentation was carried out using two different architectures, namely U-Net and LinkNet, to compare the performance of the two. After training, both architectures can process object segmentation properly. The best performance was produced by U-Net with a pixel accuracy of 99.83%, Mean IU of 89.71%, FPR of 0.91%, Precision of 85.78%, Recall of 85.31%, and F1 Score of 85.31%. With this accuracy, cross-validation was carried out using a 10-Fold. The experimental results show that U-Net gives better results in terms of transcerebellar image segmentation. After the segmentation process, an initial reconstruction is carried out using the PiFUHD architecture which produces a three-dimensional object. The results of this reconstruction are then taken from four sides to be introduced to the 3D-FHNet architecture, because the main concept of 3DFHNet is to use multiview input. To measure the accuracy of the model, the IoU (Intersection over Union) metric is used for the ground truth obtained from the PiFUHD method. This metric is used to compare the similarities between the reconstruction results and the ground truth, so that information can be obtained about the extent to which the model has succeeded in reconstructing three-dimensional objects accurately. Model performance can be calculated by using the input as ground truth or the IoU (Intersection over Union) method with an average of 76.76%. By using the 3D-FHNet method, three-dimensional image reconstruction of the ultrasound image of the fetal head can be performed accurately. The experimental results show that the 3D-FHNet method produces a more accurate three-dimensional representation of the transcerebellar image compared to the previous method. These results demonstrate great potential in improving diagnosis and treatment planning in neurology, as well as making important contributions to the development of medical image processing technologies. [ABSTRACT FROM AUTHOR]

Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parcels regardless of their shape, pattern, or layout. The core of the algorithm is the utilisation of RGB-D technology for detecting the primary boundary lines regardless of obstacles such as adhesive labels, tapes, or parcel surface patterns. For cases where detecting the boundary lines is difficult owing to narrow gaps between parcels, we propose using deep learning-based boundary line detection through the You Only Look at Coefficients (YOLACT) model. Using image segmentation techniques, the algorithm efficiently predicts boundary lines, enabling the accurate detection of irregularly sized parcels with complex surface patterns. Furthermore, even for rotated parcels, we can extract their edges through complex mathematical operations using the depth values of the specified position, enabling the detection of the wider surfaces of the rotated parcels. Finally, we validate the accuracy and real-time performance of our proposed method through various case studies, achieving mAP (50) values of 93.8% and 90.8% for randomly sized and rotationally covered boxes with diverse colours and patterns, respectively. [ABSTRACT FROM AUTHOR]

Biomedical image segmentation is a vital task in the analysis of medical imaging, including the detection and delineation of pathological regions or anatomical structures within medical images. It has played a pivotal role in a variety of medical applications, involving diagnoses, monitoring of diseases, and treatment planning. Conventionally, clinicians or expert radiologists have manually conducted biomedical image segmentation, which is prone to human error, subjective, and time-consuming. With the advancement in computer vision and deep learning (DL) algorithms, automated and semi-automated segmentation techniques have attracted much research interest. DL approaches, particularly convolutional neural networks (CNN), have revolutionized biomedical image segmentation. With this motivation, we developed a novel equilibrium optimization algorithm with a deep learning-based biomedical image segmentation (EOADL-BIS) technique. The purpose of the EOADL-BIS technique is to integrate EOA with the Faster RCNN model for an accurate and efficient biomedical image segmentation process. To accomplish this, the EOADL-BIS technique involves Faster R-CNN architecture with ResNeXt as a backbone network for image segmentation. The region proposal network (RPN) proficiently creates a collection of a set of region proposals, which are then fed into the ResNeXt for classification and precise localization. During the training process of the Faster RCNN algorithm, the EOA was utilized to optimize the hyperparameter of the ResNeXt model which increased the segmentation results and reduced the loss function. The experimental outcome of the EOADL-BIS algorithm was tested on distinct benchmark medical image databases. The experimental results stated the greater efficiency of the EOADL-BIS algorithm compared to other DL-based segmentation approaches. [ABSTRACT FROM AUTHOR]

Image segmentation is the process of partitioning an image into homogenous regions, and represents one of the most fundamental and important procedures in image processing. Image denoising is a process to remove unwanted noise from a digital image, enhancing its visual quality. Various algorithms, like non-local means and deep learning-based approaches, have been developed to remove noise while preserving important image details. Currently, the prevalent application of pattern recognition technology is achieved through the implementation of image segmentation algorithms. In this study, we present a new, highly efficient, and fast computational scheme specifically developed for a phase-field mathematical model of image segmentation. The numerical methodology is based on an operator splitting method (OSM). The split operators are solved by using closed-form analytic solutions and a finite difference method (FDM) with an alternating direction explicit (ADE) method. To show the notable efficiency and rapid computational performance of the proposed computational algorithm, we conduct a series of numerical experiments. Through these computational tests, we confirm a significant contribution to the advancement of methodologies employed in the critical domain of image processing. [ABSTRACT FROM AUTHOR]

Corneal ulcer is a prevalent ocular condition that requires ophthalmologists to diagnose, assess, and monitor symptoms. During examination, ophthalmologists must identify the corneal ulcer area and evaluate its severity by manually comparing ocular staining images with severity indices. However, manual assessment is time-consuming and may provide inconsistent results. Variations can occur with repeated evaluations of the same images or with grading among different evaluators. To address this problem, we propose an automated corneal ulcer grading system for ocular staining images based on deep learning techniques and the Hough Circle Transform. The algorithm is structured into two components for cornea segmentation and corneal ulcer segmentation. Initially, we apply a deep learning method combined with the Hough Circle Transform to segment cornea areas. Subsequently, we develop the corneal ulcer segmentation model using deep learning methods. In this phase, the predicted cornea areas are utilized as masks for training the corneal ulcer segmentation models during the learning phase. Finally, this algorithm uses the results from these two components to determine two outputs: (1) the percentage of the ulcerated area on the cornea, and (2) the severity degree of the corneal ulcer based on the Type–Grade (TG) grading standard. These methodologies aim to enhance diagnostic efficiency across two key aspects: (1) ensuring consistency by delivering uniform and dependable results, and (2) enhancing robustness by effectively handling variations in eye size. In this research, our proposed method is evaluated using the SUSTech-SYSU public dataset, achieving an Intersection over Union of 89.23% for cornea segmentation and 82.94% for corneal ulcer segmentation, along with a Mean Absolute Error of 2.51% for determining the percentage of the ulcerated area on the cornea and an Accuracy of 86.15% for severity grading.

Background: Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography. Methods: Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods. Results: Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD. Conclusions: Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing. [ABSTRACT FROM AUTHOR]

Pneumonia is a common and challenging disease to treat. Diagnosis of pneumonia is performed by analysing chest X-ray images with a specialist doctor today. This situation can create an excessive workload for doctors and prolong the diagnosis time. Performing early and accurate diagnosis of pneumonia using pre-trained deep learning models, which are a subcategory of the deep learning method, can be extremely beneficial. Using computeraided diagnosis systems increases the accuracy of pneumonia diagnosis and thanks to these systems, doctors have an idea about the disease before diagnosis. In this study chest X-Ray images were classified as healthy or pneumonia using pre-trained deep learning methods. The histogram equalisation image processing method was used to improve image quality and the mask region-based convolutional neural network pre-trained method was used to segment the chest region. Alexnet, ResNet18 and VGG16 pre-trained models were used for image classification as healthy and pneumonia. ResNet18 showed outstanding performance in this study. According to the performance metrics of accuracy (0.983), recall (0.994) and F1-score (0.987), success rates were achieved by using the ResNet18 model. This study has shown that deep learning models can achieve high success rates in pneumonia diagnosis. [ABSTRACT FROM AUTHOR]

As we all know day by day the technology is getting better and better, the production of counterfeit currency has been rapidly increasing. The counterfeit currency problem is faced by almost all countries. Since the real economy is affected, it has affected the economy of the country. Even when the drastic step of demonetization was taken in 2016 to overcome counterfeit currency, this problem did not end. The only one solution for this problem for a common man is to detect the fake currency, by using the fake currency detector machine. These machines are used in banks and large scale business, but for small scale businesses or for a common man these machines are not affordable. There are lot of researches taking place on this matter by using deep learning, image processing and machine learning techniques. This paper gives the complete methodology of fake note detector machine, which is affordable even for a common man. By implementing the applications of image processing techniques we can find out whether the currency notes are fake or not. Image processing technique consists of a number of operations that can be performed on an image, some of which include image segmentation, edge detection, gray scale conversion, preprocessing etc. The proposed system will detect the counterfeit currency of new denominations by distinguishing each denomination based on its size and depending on the features of each currency the comparison takes place. Based on the features matched, it detects whether the currency is counterfeit or not. The system have advantages like simplicity, reliability and cost effective. Which is affordable by a common man since the common man is the one who will be effected most, when the counterfeit currency are circulated in the market because he has to pay the real value of that currency. [ABSTRACT FROM AUTHOR]

Floe size distribution (FSD) has become a parameter of great interest in observations of sea ice because of its importance in affecting climate change, marine ecosystems, and human activities in the polar ocean. A most effective way to monitor FSD in the ice-covered regions is to apply image processing techniques to airborne and satellite remote sensing data, where the segmentation of individual ice floes is a challenge in obtaining FSD from remotely sensed images. In this study, we adopt a deep learning (DL) semantic segmentation network to fast and adaptive implement the task of ice floe instance segmentation. In order to alleviate the costly and time-consuming data annotation problem of model training, classical image processing technique is applied to automatically label ice floes in local-scale marginal ice zone (MIZ). Several state-of-the-art (SoA) semantic segmentation models are then trained on the labelled MIZ dataset and further applied to additional large-scale optical Sentinel-2 images to evaluate their performance in floe instance segmentation and to determine the best model. A post-processing algorithm is also proposed in our work to refine the segmentation. Our approach has been applied to both airborne and high-resolution optical (HRO) satellite images to derive acceptable FSDs at local and global scales. [ABSTRACT FROM AUTHOR]

Intelligent monitoring of machine chips can help the machine tool to remove chip accumulation in time. Based on chip shape information, real-time monitoring of the machine tool is also possible. Although image segmentation algorithms have been widely used in industry, they still need to improve. These methods still have some limitations in effectively combining global and local information of industrial images, and there needs to be an image segmentation algorithm specifically for machine tool chips. In this paper, we propose a deep learning-based algorithm for lightweight chip image segmentation named Hybridformer. First, the algorithm generates scale-aware semantic features using feature maps of different scales as the input part. Then, we use the cross specification and SelfNorm to improve the generalization robustness of the model in terms of distribution bias. Finally, the network extracts global contextual information fusing EfficientNetv2 features to achieve accurate localization. Widespread experimental results show that Hybridformer outperforms both CNN and Transformer algorithms in terms of comprehensive performance on multiple chip image datasets, and it can achieve a good balance between accuracy and size of parameters. [ABSTRACT FROM AUTHOR]

Remote sensing image segmentation plays an important role in many industrial-grade image processing applications. However, the problem of uncertainty caused by intraclass heterogeneity and interclass blurring is prevalent in high-resolution remote sensing images. Moreover, the complexity of information in high-resolution remote sensing images leads to a large amount of background information around objects. To solve this problem, a new fuzzy convolutional neural network is proposed in this paper. This network resolves the ambiguity and uncertainty of feature information by introducing a fuzzy neighbourhood module in the deep learning network structure. In addition, it adds a multi-attention gating module to highlight small object features and separate them from the complex background information to achieve fine segmentation of high-resolution remote sensing images. Experimental results on three different segmentation datasets suggest that the proposed method has higher segmentation accuracy and better performance than other deep learning networks, especially for complicated shadow information. Code will be provided in (). [ABSTRACT FROM AUTHOR]

COVID‐19 has had a profound global impact, necessitating the development of infection detection systems based on machine learning. This paper presents a Multi‐task architecture that addresses the classification and segmentation tasks for COVID‐19 detection. The model comprises an encoder for feature representation, a decoder for segmentation, and a multi‐layer perceptron for classification. Evaluations conducted on two datasets demonstrate the model's performance in both classification and segmentation. To enhance efficiency and diagnosis accuracy, CT‐scan images undergo pre‐processing using image processing algorithms like histogram equalization, median filtering, and mathematical morphology operations. The combination of the median filter pre‐processing and the proposed model yields impressive results in the classification task, achieving high accuracy, sensitivity, and specificity, with values of 0.97, 0.97, and 0.96, respectively, for dataset 1, and 0.96 in mentioned metrics for dataset 2. For segmentation, the proposed model, particularly with the average morphology pre‐processing, exhibits excellent performance with high accuracy, low mean squared error, high peak signal‐to‐noise ratio, high structural similarity index, and a mean dice coefficient of 88.86 ± 0.05 for dataset 1, and 87.97 ± 0.02 for dataset 2. Furthermore, the pre‐trained models consistently demonstrate the superiority of the median filter and proposed model in the classification task on the same datasets. In conclusion, the proposed multi‐task model, incorporating image processing techniques, achieves remarkable results in both classification and segmentation. The utilization of pre‐processing algorithms and the multi‐task framework significantly contribute to superior performance metrics. This study encourages further exploration of combining diverse image processing algorithms to advance infection diagnosis and treatment. [ABSTRACT FROM AUTHOR]

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting "black box" models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches. Deep learning frameworks require large human-annotated datasets for training and the resulting 'black box' models are difficult to interpret. Here, the authors present Kartezio; a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines. [ABSTRACT FROM AUTHOR]

Segmentation is one of the image processing techniques, widely used in computer vision, to extract various types of information represented as objects or areas of interest. The development of neural networks has influenced image processing techniques, including creation of new ways of image segmentation. The aim of this study is to compare classical algorithms and deep learning methods in RGB image segmentation tasks. Two hypotheses were put forward: (1) The quality of segmentation applying deep learning methods is higher than using classical methods for RGB images, and (2) The increase of the RGB image resolution has positive impact on the segmentation quality. Two traditional segmentation algorithms (Thresholding and K-means) were compared with deep learning approach (U-Net, SegNet and FCN 8) to verify RGB segmentation quality. Two resolutions of images were taken into consideration: 160x240 and 320x480 pixels. Segmentation quality for each algorithm was estimated based on four parameters: Accuracy, Precision, Recall and Sorensen-Dice ratio (Dice score). In the study the Carvana dataset, containing 5,088 high-resolution images of cars, was applied. The initial set was divided into training, validation and test subsets as 60%, 20%, 20%, respectively. As a result, the best Accuracy, Dice score and Recall for images with resolution 160x240 were obtained for U-Net, achieving 99.37%, 98.56%, and 98.93%, respectively. For the same resolution the highest Precision 98.19% was obtained for FCN-8 architecture. For higher resolution, 320x480, the best mean Accuracy, Dice score, and Precision were obtained for FCN-8 network, reaching 99.55%, 99.95% and 98.85%, respectively. The highest results for classical methods were obtained for Threshold algorithm reaching 80.41% Accuracy, 58.49% Dice score, 67.32% Recall and 52.62% Precision. The results confirm both hypotheses. [ABSTRACT FROM AUTHOR]

Abstract Background Grain count is crucial to wheat yield composition and estimating yield parameters. However, traditional manual counting methods are time-consuming and labor-intensive. This study developed an advanced deep learning technique for the segmentation counting model of wheat grains. This model has been rigorously tested on three distinct wheat varieties: ‘Bainong 307’, ‘Xinmai 26’, and ‘Jimai 336’, and it has achieved unprecedented predictive counting accuracy. Method The images of wheat ears were taken with a smartphone at the late stage of wheat grain filling. We used image processing technology to preprocess and normalize the images to 480*480 pixels. A CBAM-HRNet wheat grain segmentation counting deep learning model based on the Convolutional Block Attention Module (CBAM) was constructed by combining deep learning, migration learning, and attention mechanism. Image processing algorithms and wheat grain texture features were used to build a grain counting and predictive counting model for wheat grains. Results The CBAM-HRNet model using the CBAM was the best for wheat grain segmentation. Its segmentation accuracy of 92.04%, the mean Intersection over Union (mIoU) of 85.21%, the category mean pixel accuracy (mPA) of 91.16%, and the recall rate of 91.16% demonstrate superior robustness compared to other models such as HRNet, PSPNet, DeeplabV3+ , and U-Net. Method I for spike count, which calculates twice the number of grains on one side of the spike to determine the total number of grains, demonstrates a coefficient of determination R2 of 0.85, a mean absolute error (MAE) of 1.53, and a mean relative error (MRE) of 2.91. In contrast, Method II for spike count involves summing the number of grains on both sides to determine the total number of grains, demonstrating a coefficient of determination R2 of 0.92, an MAE) of 1.15, and an MRE) of 2.09%. Conclusions Image segmentation algorithm of the CBAM-HRNet wheat spike grain is a powerful solution that uses the CBAM to segment wheat spike grains and obtain richer semantic information. This model can effectively address the challenges of small target image segmentation and under-fitting problems in training. Additionally, the spike grain counting model can quickly and accurately predict the grain count of wheat, providing algorithmic support for efficient and intelligent wheat yield estimation.

In recent years, the use of deep learning technology for image processing has become mainstream, and the U-Net network has received widespread attention owing to its unique U-shaped structure, which has achieved excellent results in the field of image segmentation, especially in medical image segmentation. To enhance the performance of the U-Net network model and establish better U-Net design variables, in this paper, we propose a fuzzy-controlled multicellular gene expression programming algorithm to automatically build and optimize the U-Net. The algorithm creates an efficient variable-length gene code, generates chromosomes for the optimization of U-Net design variables, decodes the chromosomes to construct the U-Net model, dynamically calculates population fitness and fuzzy affiliation values, and achieves the optimal U-Net network through continuous evolution. The experimental results indicate that the proposed algorithm outperforms U-Net, Fully Convolutional Networks32, and VanillaUnet in image recognition segmentation, especially in the segmentation of COVID-19 CT medical images. [ABSTRACT FROM AUTHOR]

Background: Image segmentation pipelines are commonly used in microscopy to identify cellular compartments like nucleus and cytoplasm, but there are few standards for comparing segmentation accuracy across pipelines. The process of selecting a segmentation assessment pipeline can seem daunting to researchers due to the number and variety of metrics available for evaluating segmentation quality. Results: Here we present automated pipelines to obtain a comprehensive set of 69 metrics to evaluate segmented data and propose a selection methodology for models based on quantitative analysis, dimension reduction or unsupervised classification techniques and informed selection criteria. Conclusion: We show that the metrics used here can often be reduced to a small number of metrics that give a more complete understanding of segmentation accuracy, with different groups of metrics providing sensitivity to different types of segmentation error. These tools are delivered as easy to use python libraries, command line tools, Common Workflow Language Tools, and as Web Image Processing Pipeline interactive plugins to ensure a wide range of users can access and use them. We also present how our evaluation methods can be used to observe the changes in segmentations across modern machine learning/deep learning workflows and use cases. [ABSTRACT FROM AUTHOR]

Cloud and cloud shadow segmentation is one of the most critical challenges in remote sensing image processing. Because of susceptibility to factors such as disturbance from terrain features and noise, as well as a poor capacity to generalize, conventional deep learning networks, when directly used to cloud and cloud shade detection and division, have a tendency to lose fine features and spatial data, leading to coarse segmentation of cloud and cloud shadow borders, false detections, and omissions of targets. To address the aforementioned issues, a multi-scale strip feature attention network (MSFANet) is proposed. This approach uses Resnet18 as the backbone for obtaining semantic data at multiple levels. It incorporates a particular attention module that we name the deep-layer multi-scale pooling attention module (DMPA), aimed at extracting multi-scale contextual semantic data, deep channel feature information, and deep spatial feature information. Furthermore, a skip connection module named the boundary detail feature perception module (BDFP) is introduced to promote information interaction and fusion between adjacent layers of the backbone network. This module performs feature exploration on both the height and width dimensions of the characteristic pattern to enhance the recovery of boundary detail intelligence of the detection targets. Finally, during the decoding phase, a self-attention module named the cross-layer self-attention feature fusion module (CSFF) is employed to direct the aggregation of deeplayer semantic feature and shallow detail feature. This approach facilitates the extraction of feature information to the maximum extent while conducting image restoration. The experimental outcomes unequivocally prove the efficacy of our network in effectively addressing complex cloud-covered scenes, showcasing good performance across the cloud and cloud shadow datasets, the HRC_WHU dataset, and the SPARCS dataset. Our model outperforms existing methods in terms of segmentation accuracy, underscoring its paramount importance in the field of cloud recognition research. [ABSTRACT FROM AUTHOR]

Diagnosing and differentiating gastric cancer cells from stomach ulcers requires high-domain expertise and is time-consuming. Furthermore, medical image processing requires extremely high segmentation accuracy, which may lack interpretability and therefore, cannot be trusted by professional doctors for clinical application Hence, a diagnosis support system is proposed to aid pathologists and gastroenterologists that employs a deep learning network known as Multi-Information Fusion Network (MIFNET) for detecting cancer cells from histopathological images. The proposed system will offer assistance to gastroenterologists by displaying the severity level of the cancer, thereby helping them in choosing the appropriate treatment to increase the patient survival rates for those with stomach cancer. In contrast to conventional MIFNET segmentation functionality, the Fusion net layer of MIFNET was modified with VGG16 for classifying the malignant lesions in the gastric lining. The proposed MIFNET-based model results in a segmentation accuracy of 83% and a classification accuracy of 99%. Moreover, it is proven that the model achieved better prediction accuracy than other deep learning models such as U-Net and V-Net. [ABSTRACT FROM AUTHOR]

The Frangi neuron proposed in this work is a complex element that allows high-level Hessian-based image processing. Its adaptive parameters (weights) can be trained using a minimum number of training data. In our experiment, we showed that just one image is enough to optimize the values of the weights. An intuitive application of the Frangi neuron is to use it in image segmentation process. In order to test the performance of the Frangi neuron, we used diverse medical datasets on which second-order structures are visualized. The Frangi network presented in this paper trained on a single image proved to be significantly more effective than the U-net trained on the same dataset. For the datasets tested, the network performed better as measured by area under the curve receiver operating characteristic (ROC AUC) than U-net and the Frangi algorithm. However, the Frangi network performed several times faster than the non-GPU implementation of Frangi. There is nothing to prevent the Frangi neuron from being used as part of any other network as a component to process two-dimensional images, for example, to detect certain second-order features in them. [ABSTRACT FROM AUTHOR]

We investigated various methods for image segmentation and image processing for the segmentation of MRI of human medical data, as well as bioinformatics for the segmentation of brain cell details, in this work. The goal is to demonstrate and bring various mathematical analyses for medical and biological image analysis. We proposed new software and methods for improving the segmentation of biological and medical data. This way, we can fi nd new ways to improve the diagnostic process in medical data and improve results in cell and iron diagnostics. We present the GrabCut algorithm as well as new, improved software for this part, a fuzzy approach and fuzzy processing of tissues, and fi nally machine-learning techniques with neural networks. We implemented the new software in the C++ programming language for the Grab cut algorithm. Consequently, we present a fuzzy approach to the diagnosis of image data in Matlab. Finally, a deep learning-based approach is used, with a U-Net-based segmentation architecture proposed to measure the various brain cell parameters. We will be able to proceed with data that we were unable to proceed when using other methods. As a result, we improved biological and medical data segmentation to obtain better boundaries and sharper edges on the objects. There is still space to extend these methods to other medical and biological applications (Tab. 1, Fig. 34, Ref. 46). Text in PDF www.elis.sk. [ABSTRACT FROM AUTHOR]

Punching is a process that is sensitive to a multitude of parameters. The estimation of part and punch quality is often based on expert knowledge and trial-and-error methods, mostly carried out as a separate offline process analysis. In a previous study, we developed an optical inline monitoring system with subsequent image processing which showed promising results in terms of capturing every manufactured part, but was limited by slow image processing. Here, we present a more efficient image processing technique based on neural networks. For our approach, we manually identify the burnish parts in images based on criteria established via an expert survey in order to generate a training dataset. We then employ a combination of region-based and boundary-based losses to optimize the neural network towards a segmentation of the burnish surface which allows for an accurate measurement of the burnish height. The hyperparameter optimization is based on custom evaluation metrics that reflect the requirements of the burnish surface identification problem as well. After comparing different neural network architectures, we focus on optimizing the backbone of the UNet++ structure for our task. The promising results demonstrate that neural networks are indeed capable of an inline segmentation that can be used for measuring the burnish surface of punching parts. [ABSTRACT FROM AUTHOR]

Background: Grain count is crucial to wheat yield composition and estimating yield parameters. However, traditional manual counting methods are time-consuming and labor-intensive. This study developed an advanced deep learning technique for the segmentation counting model of wheat grains. This model has been rigorously tested on three distinct wheat varieties: 'Bainong 307', 'Xinmai 26', and 'Jimai 336', and it has achieved unprecedented predictive counting accuracy. Method: The images of wheat ears were taken with a smartphone at the late stage of wheat grain filling. We used image processing technology to preprocess and normalize the images to 480*480 pixels. A CBAM-HRNet wheat grain segmentation counting deep learning model based on the Convolutional Block Attention Module (CBAM) was constructed by combining deep learning, migration learning, and attention mechanism. Image processing algorithms and wheat grain texture features were used to build a grain counting and predictive counting model for wheat grains. Results: The CBAM-HRNet model using the CBAM was the best for wheat grain segmentation. Its segmentation accuracy of 92.04%, the mean Intersection over Union (mIoU) of 85.21%, the category mean pixel accuracy (mPA) of 91.16%, and the recall rate of 91.16% demonstrate superior robustness compared to other models such as HRNet, PSPNet, DeeplabV3+ , and U-Net. Method I for spike count, which calculates twice the number of grains on one side of the spike to determine the total number of grains, demonstrates a coefficient of determination R2 of 0.85, a mean absolute error (MAE) of 1.53, and a mean relative error (MRE) of 2.91. In contrast, Method II for spike count involves summing the number of grains on both sides to determine the total number of grains, demonstrating a coefficient of determination R2 of 0.92, an MAE) of 1.15, and an MRE) of 2.09%. Conclusions: Image segmentation algorithm of the CBAM-HRNet wheat spike grain is a powerful solution that uses the CBAM to segment wheat spike grains and obtain richer semantic information. This model can effectively address the challenges of small target image segmentation and under-fitting problems in training. Additionally, the spike grain counting model can quickly and accurately predict the grain count of wheat, providing algorithmic support for efficient and intelligent wheat yield estimation. [ABSTRACT FROM AUTHOR]

Introduction: Leaves are important organs for photosynthesis in plants, and the restriction of leaf growth is among the earliest visible effects under abiotic stress such as nutrient deficiency. Rapidly and accurately monitoring plant leaf area is of great importance in understanding plant growth status in modern agricultural production. Method: In this paper, an image processing-based non-destructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring Brassica napus (rapeseed) leaf area is proposed. A total of 1,080 rapeseed leaf image areas from five nutrient amendment treatments were continuously collected using the automatic leaf acquisition device and the commonly used area measurement methods (manual and stretching methods). Results: The average error rate of the manual method is 12.12%, the average error rate of the stretching method is 5.63%, and the average error rate of the splint method is 0.65%. The accuracy of the automatic leaf acquisition device was improved by 11.47% and 4.98% compared with the manual and stretching methods, respectively, and had the advantages of speed and automation. Experiments on the effects of the manual method, stretching method, and splinting method on the growth of rapeseed are conducted, and the growth rate of rapeseed leaves under the stretching method treatment is considerably greater than that of the normal treatment rapeseed. Discussion: The growth rate of leaves under the splinting method treatment was less than that of the normal rapeseed treatment. The mean intersection over union (mIoU) of the UNet-Attention model reached 90%, and the splint method had higher prediction accuracy with little influence on rapeseed. [ABSTRACT FROM AUTHOR]

In the latest research progress, deep neural networks have been revolutionized by frameworks to extract image features more accurately. In this study, we focus on an attention model that can be useful in deep neural networks and propose a simple but strong feature extraction deep network architecture, W-Net. The architecture of our W-Net network has two mutually independent path structures, and it is designed with the following advantages. (1) There are two independent effective paths in our proposed network structure, and the two paths capture more contextual information from different scales in different ways. (2) The two paths acquire different feature images, and in the upsampling approach, we use bilinear interpolation thus reducing the feature map distortion phenomenon and integrating the different images processed. (3) The feature image processing is at a bottleneck, and a hierarchical attention module is constructed at the bottleneck by reclassifying after the channel attention module and the spatial attention module, resulting in more efficient and accurate processing of feature images. During the experiment, we also tested iSAID, a massively high spatial resolution remote sensing image dataset, with further experimental data comparison to demonstrate the generality of our method for remote sensor image segmentation. [ABSTRACT FROM AUTHOR]

Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. [ABSTRACT FROM AUTHOR]

The advancement of 6G networks is driven by the need for customer-centric communication and network control, particularly in applications such as intelligent transport systems. These applications rely on outdoor communication in extremely high-frequency (EHF) bands, including millimeter wave (mmWave) frequencies exceeding 30 GHz. However, EHF signals face challenges such as higher attenuation, diffraction, and reflective losses caused by obstacles in outdoor environments. To overcome these challenges, 6G networks must focus on system designs that enhance propagation characteristics by predicting and mitigating diffraction, reflection, and scattering losses. Strategies such as proper handovers, antenna orientation, and link adaptation techniques based on losses can optimize the propagation environment. Among the network components, aerial networks, including unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOL), are particularly susceptible to diffraction losses due to surrounding buildings in urban and suburban areas. Traditional statistical models for estimating the height of tall objects like buildings or trees are insufficient for accurately calculating diffraction losses due to the dynamic nature of user mobility, resulting in increased latency unsuitable for ultra-low latency applications. To address these challenges, this paper proposes a deep learning framework that utilizes easily accessible Google Street View imagery to estimate building heights and predict diffraction losses across various locations. The framework enables real-time decision-making to improve the propagation environment based on users' locations. The proposed approach achieves high accuracy rates, with an accuracy of 39% for relative error below 2%, 83% for relative error below 4%, and 96% for both relative errors below 7% and 10%. Compared to traditional statistical methods, the proposed deep learning approach offers significant advantages in height prediction accuracy, demonstrating its efficacy in supporting the development of 6G networks. The ability to accurately estimate heights and map diffraction losses before network deployment enables proactive optimization and ensures real-time decision-making, enhancing the overall performance of 6G systems. [ABSTRACT FROM AUTHOR]

Food quality control is an important task in the agricultural domain at the postharvest stage for avoiding food losses. The latest achievements in image processing with deep learning (DL) and computer vision (CV) approaches provide a number of effective tools based on the image colorization and image-to-image translation for plant quality control at the postharvest stage. In this article, we propose the approach based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) techniques to use synthesized and segmented VNIR imaging data for early postharvest decay and fungal zone predictions as well as the quality assessment of stored apples. The Pix2PixHD model achieved higher results in terms of VNIR images translation from RGB (SSIM = 0.972). Mask R-CNN model was selected as a CNN technique for VNIR images segmentation and achieved 58.861 for postharvest decay zones, 40.968 for fungal zones and 94.800 for both the decayed and fungal zones detection and prediction in stored apples in terms of F1-score metric. In order to verify the effectiveness of this approach, a unique paired dataset containing 1305 RGB and VNIR images of apples of four varieties was obtained. It is further utilized for a GAN model selection. Additionally, we acquired 1029 VNIR images of apples for training and testing a CNN model. We conducted validation on an embedded system equipped with a graphical processing unit. Using Pix2PixHD, 100 VNIR images from RGB images were generated at a rate of 17 frames per second (FPS). Subsequently, these images were segmented using Mask R-CNN at a rate of 0.42 FPS. The achieved results are promising for enhancing the food study and control during the postharvest stage. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to investigate the detection and diagnosis of plant diseases using Deep Learning and Digital Image Processing. Previous research has primarily focused on single plant disease scenarios using publicly available datasets, often overlooking the image preprocessing phase. In this study, we propose a model that works with 10 different plants and utilizes approximately 50,000 images for training and testing. We classified 36 distinct classes into healthy or infected types based on disease labels. To enhance the accuracy of disease detection, we recommend employing image processing techniques and considering multiple plant scenarios. We utilized a dual-layer Convolutional Neural Network (CNN) for the publicly available dataset and supplemented it with real-time images captured from various farms in Village Rancharda Near Ahmedabad, Gujarat, India (PIN: 38255). Our research introduces several novel elements in the preprocessing steps. We employed HSV segmentation, flood fills segmentation, and a proposed deep learning model for image segmentation. Additionally, we standardized the resolution of all images to ensure uniformity. These preprocessing techniques refine the image data required for accurate classification and enhance the visibility of diseased portions. For image processing, we employed a sliding window mean average deviation technique and stacked the processed images onto the original image, resulting in six-channel images. Our proposed model demonstrates improved performance on the validation data, achieving an accuracy of up to 97.95%. Furthermore, we transformed this model into a TFLite model, which can be easily integrated into client applications without the need for a server. In our case, we implemented the model on an Android platform. These findings indicate the potential of our proposed model to significantly enhance the detection and diagnosis of plant diseases in real-world scenarios. [ABSTRACT FROM AUTHOR]

In remote-sensing image processing tasks, images with higher resolution always result in better performance on downstream tasks, such as scene classification and object segmentation. However, objects in remote-sensing images often have low resolution and complex textures due to the imaging environment. Therefore, effectively reconstructing high-resolution remote-sensing images remains challenging. To address this concern, we investigate embedding context information and object priors from remote-sensing images into current deep learning super-resolution models. Hence, this paper proposes a novel remote-sensing image super-resolution method called Context-Guided Constrained Network (CGC-Net). In CGC-Net, we first design a simple but effective method to generate inverse distance maps from the remote-sensing image segmentation maps as prior information. Combined with prior information, we propose a Global Context-Constrained Layer (GCCL) to extract high-quality features with global context constraints. Furthermore, we introduce a Guided Local Feature Enhancement Block (GLFE) to enhance the local texture context via a learnable guided filter. Additionally, we design a High-Frequency Consistency Loss (HFC Loss) to ensure gradient consistency between the reconstructed image (HR) and the original high-quality image (HQ). Unlike existing remote-sensing image super-resolution methods, the proposed CGC-Net achieves superior visual results and reports new state-of-the-art (SOTA) performance on three popular remote-sensing image datasets, demonstrating its effectiveness in remote-sensing image super-resolution (RSI-SR) tasks. [ABSTRACT FROM AUTHOR]

Cracks are fractures or breaks that occur in materials such as concrete, metals, rocks, and other solids. Various methods are used to detect and monitor cracks; among many of them, image-based methodologies allow fast identification of the distress and easy quantification of the percentage of cracks in the scene. Two main categories can be identified: classical and deep learning approaches. In the last decade, the tendency has moved towards the use of the latter. Even though they have proven their outstanding predicting performance, they suffer some drawbacks: a "black-box" nature leaves the user blind and without the possibility of modifying any parameters, a huge amount of labeled data is generally needed, a process that requires expert judgment is always required, and, finally, they tend to be time-consuming. Accordingly, the present study details the methodology for a new algorithm for crack segmentation based on the theory of minimal path selection combined with a region-based approach obtained through the segmentation of texture features extracted using Gabor filters. A pre-processing step is described, enabling the equalization of brightness and shadows, which results in better detection of local minima. These local minimal are constrained by a minimum distance between adjacent points, enabling a better coverage of the cracks. Afterward, a region-based segmentation technique is introduced to determine two areas that are used to determine threshold values used for rejection. This step is critical to generalize the algorithm to images presenting close-up scenes or wide cracks. Finally, a geometrical thresholding step is presented, allowing the exclusion of rounded areas and small isolated cracks. The results showed a very competitive F1-score (0.839), close to state-of-the-art values achieved with deep learning techniques. The main advantage of this approach is the transparency of the workflow, contrary to what happens with deep learning frameworks. In the proposed approach, no prior information is required; however, the statistical parameters may have to be adjusted to the particular case and requirements of the situation. The proposed algorithm results in a useful tool for researchers and practitioners needing to validate their results against some reference or needing labeled data for their models. Moreover, the current study could establish the grounds to standardize the procedure for crack segmentation with a lower human bias and faster results. The direct application of the methodology to images obtained with any low-cost sensor makes the proposed algorithm an operational support tool for authorities needing crack detection systems in order to monitor and evaluate the current state of the infrastructures, such as roads, tunnels, or bridges. [ABSTRACT FROM AUTHOR]

The deep learning-based image segmentation approach has evolved into the mainstream of target detection and shape characterization in microscopic image analysis. However, the accuracy and generalizability of deep learning approaches are still hindered by the insufficient data problem that results from the high expense of human and material resources for microscopic image acquisition and annotation. Generally, image augmentation can increase the amount of data in a short time by means of mathematical simulation, and has become a necessary module for deep learning-based material microscopic image analysis. In this work, we first review the commonly used image augmentation methods and divide more than 60 basic image augmentation methods into eleven categories based on different implementation strategies. Secondly, we conduct experiments to verify the effectiveness of various basic image augmentation methods for the image segmentation task of two classical material microscopic images using evaluation metrics with different applicabilities. The U-Net model was selected as a representative benchmark model for image segmentation tasks, as it is the classic and most widely used model in this field. We utilize this model to verify the improvement of segmentation performance by various augmentation methods. Then, we discuss the advantages and applicability of various image augmentation methods in the material microscopic image segmentation task. The evaluation experiments and conclusions in this work can serve as a guide for the creation of intelligent modeling frameworks in the materials industry. [ABSTRACT FROM AUTHOR]

Calorie estimation is of significant importance in promoting a healthy lifestyle, as it enables individuals to effectively manage their weight. Applications that calculate caloric intake by analyzing food images have the potential to save users time and effort. Consequently, the primary objective of this study is the development of a model capable of identifying food classes from images. This classification model is crucial for the first step of calorie estimation applications. While numerous food classification datasets are available online, there is a paucity of food segmentation datasets. In response to this challenge, a novel dataset for food segmentation is presented, designed to facilitate the estimation of food quantities—a critical component of the second step in calorie estimation. The performance of the MobileNetV2 model was evaluated for food classification, yielding an optimal accuracy of 93.06% and a loss of 0.31. These promising experimental results demonstrate the potential of the approach in real-time environments. [ABSTRACT FROM AUTHOR]

With the development of computer vision technology, its applications in forestry are steadily becoming wider. To address the problems of inconvenience in transporting unmanned aerial vehicles (UAVs), as well as the complex operation of large instruments for measurement, a new method based on multi-vision image segmentation and coordinate fusion (MISF) is proposed in this paper for measuring the size of standing trees. In MISF, after images of a standing tree are captured using a camera from multiple angles, a semantic segmentation method based on deep learning is used to segment the main body of the standing tree and automatically detect the edge feature points. Next, the effects of visual field splicing and fusion are analyzed collaboratively using the correlations among images, so as to restore the three-dimensional spatial information of the feature points of the tree to be measured. Lastly, the size attributes of the standing tree, such as height, diameter at breast height (DBH), and crown width, are automatically measured. The urban environment measurement experiment showed that the relative errors of tree height, DBH, and crown width measured using the proposed method, i.e., MISF, were 1.89%, 2.42%, and 3.15%, respectively, representing a significant enhancement compared with binocular measurement. On the one hand, the experimental results exhibited a high degree of measurement accuracy; therefore, MISF can be used for the management inventory of typical forests. On the other hand, MISF cannot be used if a tree's images cannot be acquired due to environmental or other reasons. [ABSTRACT FROM AUTHOR]

This article discusses the design and development of a neural network algorithm for the segmentation of text areas in real-scene images. After reviewing the available neural network models, the U-net model was chosen as a basis. Then an algorithm for detecting text areas in real-scene images was proposed and implemented. The experimental training of the network allows one to define the neural network parameters such as the size of input images and the number and types of the network layers. Bilateral and low-pass filters were considered as a preprocessing stage. The number of images in the KAIST Scene Text Database was increased by applying rotations, compression, and splitting of the images. The results obtained were found to surpass competing methods in terms of the F-measure value.