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Purpose: Shear-wave elastography (SWE) measures tissue elasticity using ultrasound waves. This study proposes a histogram-based SWE analysis to improve breast malignancy detection. Methods: N = 22/32 (patients/tumors) benign and n = 51/64 malignant breast tumors with histological ground truth. Colored SWE heatmaps were adjusted to a 0–180 kPa scale. Normalized, 250-binned RGB histograms were used as image descriptors based on skewness and area under curve (AUC). The histogram method was compared to conventional SWE metrics, such as (1) the qualitative 5-point scale classification and (2) average stiffness (SWEavg)/maximal tumor stiffness (SWEmax) within the tumor B-mode boundaries. Results: The SWEavg and SWEmax did not discriminate malignant lesions in this database, p > 0.05, rank sum test. RGB histograms, however, differed between malignant and benign tumors, p < 0.001, Kolmogorov–Smirnoff test. The AUC analysis of histograms revealed the reduction of soft-tissue components as a significant SWE biomarker (p = 0.03, rank sum). The diagnostic accuracy of the suggested method is still low (Se = 0.30 for Se = 0.90) and a subject for improvement in future studies. Conclusions: Histogram-based SWE quantitation improved the diagnostic accuracy for malignancy compared to conventional average SWE metrics. The sensitivity is a subject for improvement in future studies. [ABSTRACT FROM AUTHOR]

In the automatic identification of lesions, aiming at the misdiagnosis of the lung nodule size, shape, blood vessels and other lung tissues on CT images of pneumonia, this paper proposes a method for identifying lung diseases based on capsule neural networks. Considering that the texture features of lung CT images contain important medical information, this article first combines gray average, entropy, fractal dimension and fractal intercept to form feature vectors as texture features, and introduces context models to obtain context information. The LBG algorithm was used to achieve context quantization, and the CT images of lungs were preprocessed. Then, for the problem of less CT image data of lungs and fewer features of lung diseases in this paper, the existing images are enhanced and the data set is expanded. Finally, in the recognition process of pneumonia, the characteristic pose information will affect the recognition. In order to solve this problem, this paper uses a capsule neural network to identify and detect lung CT images. The application results show that the accuracy rate of identifying lung diseases by capsule neural network is 98.7%. Compared with traditional convolutional networks, capsule neural networks can better identify lung diseases. [ABSTRACT FROM AUTHOR]

In this day and age, the programmed procurement of a tag and acknowledgment assumes a significant part in observing and coordinating vehicles in significant urban communities. It is hard to recognize a driver or proprietor of a vehicle that disregards traffic controls or plays out any incidental movement out and about. It will require a great deal of investment for a cop to review the plate of every vehicle. Subsequently, a mechanized tag acknowledgment framework can tackle these sorts of issues. This is the proposed technique; two Deep Learning calculations are utilized to distinguish the permit number and characters on the tag from the constant picture. The primary YOLOv5 model tracks down the main in the live video of a vehicle out and about. Then, at that point, cut out the area of the permit numbers in the video. The cut casing is then embedded into a second SSD (Single Shot Detection) to identify slugs on that tag. The prepared model acquires a high precision of 96.2% over a sum of 400 picture databases. [ABSTRACT FROM AUTHOR]

Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases. [ABSTRACT FROM AUTHOR]

Leukemia is a deadly disease caused by the overproduction of immature white blood cells (WBS) in the bone marrow. If leukemia is detected at the initial stages, the chances of recovery are better. Typically, morphological analysis for the identification of acute lymphoblastic leukemia (ALL) is performed manually on blood cells by skilled medical personnel, which has several disadvantages, including a lack of medical personnel, sluggish analysis, and prediction that is dependent on the medical personnel's expertise. Therefore, we proposed the Multi-Attention EfficientNetV2S and EfficientNetB3 state-of-the-art deep learning architectures using transfer learning-based fine-tuning approach to distinguish the normal and blast cells from microscopic blood smear images that both are pretrained on large-scale ImageNet database. We simply modified the last block of both models and added additional layers to both models. After including this Multi-Attention Mechanism, it not only reduces the model's complexities but also generalizes its network quite well. By using the proposed technique, the accuracy has improved and the overall loss is also minimized. Our Multi-Attention EfficientNetV2S and EfficientNetB3 models achieved 99.73% and 99.25% accuracy, respectively. We have further compared the proposed model's performance to other individual and ensemble models. Upon comparison, the proposed model outclassed the existing literature and other benchmark models, thus proving its efficiency. [ABSTRACT FROM AUTHOR]

Research on deep learning models for chest radiology applications have been getting much attention by the community. These works mostly focus on developing models using in-domain data. However, real-world scenarios might not always match the training set, which makes some models perform worse at the deployment stage. This work focuses on studying the effect of dataset mismatch on chest radiography and methods that can mitigate this problem. We developed the lung balance contrast enhancement technique (lung BCET) which automatically identifies the lung region and normalizes the image accordingly to improve the robustness on out-of-domain data. We also explored augmentation methods that are suitable for chest radiography. We compiled data from multiple tuberculosis (TB) datasets to evaluate and compare the performance of the preprocessing and augmentation methods using the area under the receiver operating characteristic curve (AUC) and heatmap quality. On out-of-domain testing conditions, the lung BCET preprocessing method achieves the highest AUC scores of 0.7978 and 0.6240 for the Maesot and Bureau of TB (BT) datasets, respectively. We also found that lung BCET can also be used to perform data augmentation in conjunction with standard augmentation techniques to improve the performance in both in- and out-of-domain conditions.

At present, researchers have made great progress in the research of object detection, however, these studies mainly focus on the object detection of images under normal lighting, ignoring the target detection under low light. And images in the fields of automatic driving at night and surveillance are usually obtained in low-light environments. These images have problems such as poor brightness, low contrast, and obvious noise, which lead to a large amount of information loss in the image. And the performance of object detection in low light is reduced. In this paper, we propose a low-light image enhancement method based on multi-scale network fusion to solve the problems of images in low-light environments. Aiming at the problem that the effective information of low-light images is relatively small, we propose a preprocessing method for image nonlinear transformation and fusion, which improves the amount of available information in the light image. Then, in order to obtain a better enhancement effect, a multi-scale feature fusion method is proposed, which fuses features from different resolution levels in the network. The details of low-light areas in the image are improved, and the problem of feature loss caused by too deep network layers is solved. The experimental results show that our proposed method can achieve better enhancement effects on different datasets compared with the current mainstream methods. The average recall value of the object detection with our method is improved by 38.25%, which shows that our proposed method is effective and can promote the development of autonomous driving, monitoring, and other fields.

Image processing is one of the most rapidly evolving technologies today, and it is an approach for applying operations on an image to improve it or extract relevant information from it. This is a critical research field in the engineering and computer sciences. However, analyzing a large number of variables demands a lot of memory and processing resources, which can cause a classification algorithm to overfit the training samples and underfit the test samples. As a result, various strategies, such as extraction, can be used to reduce the number of features in a dataset by producing new features from old ones. In this paper, we first propose a deep learning-based feature extraction approach with a modular neural network, where we employ a pre-trained neural architecture search net (NASNet) as a feature extractor on a custom dataset of raw copper and cobalt images. It allows the input image to be feed-forwarded while performing feature learning and feature map and then stops at a pooling layer before the fully connected (FC) layer in the NASNet to extract and save the outputs of that layer in dumped files. Second, the extracted features are used as training data to build a deep neural network and machine learning algorithms for the image classification of copper and cobalt raw minerals. The experimental results show that the NASNet extracts the features efficiently, and the proposed modular neural network performs well with the boosting-decision tree as a classifier, which gives higher accuracy of 91% than 90% of the deep neural network; moreover, the precision is 1 higher than 0.98 for the deep neural network.

The fusion technology, combining infrared and visible modes, has the potential to enhance the semantic content of backgrounds, thereby improving scene interpretability. However, most existing image fusion algorithms primarily concentrate on the fusion process, often neglecting the importance of preprocessing the source images to enhance their visual fidelity. Additionally, these algorithms frequently overlook the distinct characteristics of infrared and visible modes, leading to suboptimal weight allocations that do not correspond with human perception. To tackle these issues, this paper proposes a fusion algorithm that emphasizes visual fidelity and the rectification of salient information. More specifically, we improve fusion algorithms by designing an adaptive enhancement method based on Taylor approximation and visual compensation, which proves particularly effective in complex environments. Our proposed multi-scale decomposition approach extracts salient information from the transmission map, thereby enriching fusion results with finer details to accentuate target features. Drawing inspiration from the distinctive attributes of infrared and visible image modes, we devise a fusion weight calculation method grounded in similarity measurements to effectively convey significant information from the source images. To validate the effectiveness of our proposed method, we conducted validation experiments using publicly available datasets. Our experimental findings exhibit a prominent advantage over fifteen state-of-the-art fusion algorithms in both subjective and objective assessments. Our code is publicly available at: https://github.com/VCMHE/FVC_SIR. [ABSTRACT FROM AUTHOR]

People of all countries, developed and developing alike endure cancer-related fatal diseases. The rate of breast cancer in females is increasing daily, partly due to ignorance and misdiagnosis in the early stages. Diagnosis of breast cancer accurately during its earlier stages of development can result in proper initial treatment for breast cancer. Artificial intelligence can aid in the acceleration and automation of breast cancer detection. Deep learning is decisive in effectively recognizing and classifying cancer on large datasets of medical images. In this paper, we propose a novel computer-aided classification approach, Mammo-Light for breast cancer prediction. Preprocessing strategies have been utilized to eradicate the noise and enhance mammogram lesions. Photometric augmentation techniques adapted to the preprocessed classes to balance and increase the size of the dataset. After that, a lightweight yet intuitive convolutional neural network is applied to classify breast cancer on the publicly available dataset CBIS-DDSM. For further validation of the proposed approach, we have used the MIAS dataset. Mammo-Light attained a 99.17% and 98.42% test accuracy respectively for CBIS-DDSM and MIAS datasets and outperformed state-of-the-art methods in terms of accuracy and other metrics. Due to being the lightweight model, Mammo-Light performs exceptionally well with fewer parameters and computational time, which can potentially contribute to the field of breast cancer early diagnosis and enable fast treatment. • Effective preprocessed procedures for removing noise and enhancing mammograms. • Balance and expand the data in a highly efficient manner. • A robust and high-accuracy lightweight deep-learning model contains only a few layers proposed. [ABSTRACT FROM AUTHOR]

The investigation of plant phenotypes through 3D modeling has emerged as a significant field in the study of automated plant phenotype acquisition. In 3D model construction, conventional image preprocessing methods exhibit low efficiency and inherent inefficiencies, which increases the difficulty of model construction. In order to ensure the accuracy of the 3D model, while reducing the difficulty of image preprocessing and improving the speed of 3D reconstruction, deep learning semantic segmentation technology was used in the present study to preprocess original images of soybean plants. Additionally, control experiments involving soybean plants of different varieties and different growth periods were conducted. Models based on manual image preprocessing and models based on image segmentation were established. Point cloud matching, distance calculation and model matching degree calculation were carried out. In this study, the DeepLabv3+, Unet, PSPnet and HRnet networks were used to conduct semantic segmentation of the original images of soybean plants in the vegetative stage (V), and Unet network exhibited the optimal test effect. The values of mIoU, mPA, mPrecision and mRecall reached 0.9919, 0.9953, 0.9965 and 0.9953. At the same time, by comparing the distance results and matching accuracy results between the models and the reference models, a conclusion could be drawn that semantic segmentation can effectively improve the challenges of image preprocessing and long reconstruction time, greatly improve the robustness of noise input and ensure the accuracy of the model. Semantic segmentation plays a crucial role as a fundamental component in enabling efficient and automated image preprocessing for 3D reconstruction of soybean plants during the vegetative stage. In the future, semantic segmentation will provide a solution for the pre-processing of 3D reconstruction for other crops.

Compared to wide-field telescopes, small-field detection systems have higher spatial resolution, resulting in stronger detection capabilities and higher positioning accuracy. When detecting by small fields in synchronous orbit, both space debris and fixed stars are imaged as point targets, making it difficult to distinguish them. In addition, with the improvement in detection capabilities, the number of stars in the background rapidly increases, which puts higher requirements on recognition algorithms. Therefore, star detection is indispensable for identifying and locating space debris in complex backgrounds. To address these difficulties, this paper proposes a real-time star extraction method based on adaptive filtering and multi-frame projection. We use bad point repair and background suppression algorithms to preprocess star images. Afterwards, we analyze and enhance the target signal-to-noise ratio (SNR). Then, we use multi-frame projection to fuse information. Subsequently, adaptive filtering, adaptive morphology, and adaptive median filtering algorithms are proposed to detect trajectories. Finally, the projection is released to locate the target. Our recognition algorithm has been verified by real star images, and the images were captured using small-field telescopes. The experimental results demonstrate the effectiveness of the algorithm proposed in this paper. We successfully extracted hip-27066 star, which has a magnitude of about 12 and an SNR of about 1.5. Compared with existing methods, our algorithm has advantages in both recognition rate and false-alarm rate, and can be used as a real-time target recognition algorithm for space-based synchronous orbit detection payloads.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

A huge amount of information exists in the form of textbooks, paper documents, newspapers, and other physical forms, that is required to be digitized for its effective access and long-time availability. Optical Character Recognition (OCR) is an effective way to digitize the text. In this study, we have used Google’s Tesseract as the OCR tool. The focus of our study is to improve Tesseract’s accuracy on machine-printed Garhwali documents by using image pre-processing techniques including Super-Resolution (SR), different binarization methods (Otsu and adaptive thresholding), skew correction, morphological operations, and ImageMagick methods. To improve the Tesseract results, we used the three proposed approaches – two approaches differed by the binarization method (Otsu and adaptive thresholding), and the third approach used ImageMagick methods for pre-processing. For evaluation purposes, we created a dataset by capturing images from a sample of five Garhwali textbooks using two mobile cameras with different resolutions; two books were captured by a high-resolution camera and the other three were captured through a low-resolution camera. Our experiments showed good results in specific cases, for high-resolution images, 88.13% accuracy was achieved for Otsu thresholding without applying the Super-Resolution and for low-resolution images, 87.44% accuracy was achieved for ImageMagick with Super-Resolution.

Early identification and adequate treatment can help prevent lung disorders from becoming chronic, severe, and life-threatening. X-ray images are commonly used and an automated and effective method involving deep learning techniques can potentially contribute to quick and accurate diagnosis of lung disorders. However, in the study of medical imaging using deep learning, two obstacles limit interpretability. One is an insufficient and imbalanced number of training samples in most medical datasets. The other is excessive training time. Although training time can be reduced by decreasing the number of pixels in the images, training with low resolution images tends to result in poor performance. This study represents a solution to overcome these impediments by balancing the number of images and reducing overall processing time while preserving accuracy. The dataset used in this research contains an unequal number of images in the different classes. The quantity of data in the classes is balanced by creating synthetic images based on the patterns and characteristics of the original images, using a Deep Convolutional Generative Adversarial Network (DCGAN). Unwanted regions are removed from the X-ray images, the brightness and contrast of the images are enhanced, and the abnormalities are highlighted by using different artifact removal, noise reduction, and enhancement techniques. We propose a Modified Compact Convolutional Transformer (MCCT) model using 32 × 32 sized images for the categorization of lung disorders into four classes. An ablation study of eleven cases is employed to adjust several hyper parameters and layer topologies. This reduces training time while preserving accuracy. Six transfer learning models, VGG19, VGG16, ResNet152, ResNet50, ResNet50V2, and MobileNet are applied with the same image size the performance is compared with the proposed MCCT model. Our MCCT model records the greatest test accuracy of 95.37%, requiring a short training time, 10-12 s/epoch, whereas the other models only reach near-moderate performance with accuracies ranging from 43% to 79% and training times of 80-90 s/epoch. The robustness of the model with regards to the number of training samples is validated by training the model multiple times reducing the number of training images gradually from 49621 images to 6204 images. Results suggest that even with a smaller dataset, the performance is sustained. Our proposed approach may contribute to an effective CAD based diagnostic system by addressing the issues of insufficient and imbalanced numbers of medical images, excessive training times and low-resolution images.

Breast cancer is the 2nd leading cancer of death among women around the world. In Asia and Africa due to low income, the mortality rates are very high as compared to Europe and America. Initially, image interpretation is manually conducted by the radiologist and physicians that requires expertise; thus, the computer-aided diagnostic is necessary to enhance the accuracy of cancer diagnostics in mammograms at early stages. To overcome human error computer-aided system was developed based on machine learning and deep learning algorithm to process medical images with efficient accuracy for the diagnosis of cancer and assist the physician for better decisions making. This research aims to present the state-of-the-art machine learning techniques for the detection of breast cancer, and critically analysis of the current literature in this area to identify the research gap. There are many studies presented in the literature to achieve similar goals. The main difference between these studies and this review is that this paper is more focused on those modalities that can figure out breast composition, mass, density, calcification, and architectural distortion. This study includes a summary of 110 papers, pointing out which techniques are applied for image preprocessing and classification, which method is implemented for the detection of breast density, mass, and calcification from mammogram images. Furthermore, we critically analyzed the performance measuring parameters for the evaluation of results and the datasets that have been used for experiments. Another focus in this review is to assess the modalities and features that can be helpful for the assessment of grading in mammogram images. [ABSTRACT FROM AUTHOR]

The primary task in calculating the tobacco shred blending ratio is identifying the four tobacco shred types: expanded tobacco silk, cut stem, tobacco silk, and reconstituted tobacco shred. The classification precision directly affects the subsequent determination of tobacco shred components. However, the tobacco shred types, especially expanded tobacco silk and tobacco silk, have no apparent differences in macro-scale characteristics. The tobacco shreds have small size and irregular shape characteristics, creating significant challenges in their recognition and classification based on machine vision. This study provides a complete set of solutions aimed at this problem for screening tobacco shred samples, taking images, image preprocessing, establishing datasets, and identifying types. A block threshold binarization method is used for image preprocessing. Parameter setting and method performance are researched to obtain the maximum number of complete samples with acceptable execution time. ResNet50 is used as the primary classification and recognition network structure. By increasing the multiscale structure and optimizing the number of blocks and loss function, a new tobacco shred image classification method is proposed based on the MS-X-ResNet (Multi-Scale-X-ResNet) network. Specifically, the MS-ResNet network is obtained by fusing the multi-scale Stage 3 low-dimensional and Stage 4 high-dimensional features to reduce the overfitting risk. The number of blocks in Stages 1-4 are adjusted from the original 3:4:6:3 to 3:4:N:3 (A-ResNet) and 3:3:N:3 (B-ResNet) to obtain the X-ResNet network, which improves the model's classification performance with lower complexity. The focal loss function is selected to reduce the impact of identification difficulty for different sample types on the network and improve its performance. The experimental results show that the final classification accuracy of the network on a tobacco shred dataset is 96.56%. The image recognition of a single tobacco shred requires 103 ms, achieving high classification accuracy and efficiency. The image preprocessing and deep learning algorithms for tobacco shred classification and identification proposed in this study provide a new implementation approach for the actual production and quality detection of tobacco and a new way for online real-time type identification of other agricultural products. [ABSTRACT FROM AUTHOR]

Underwater detection equipment with fish detection technology has broad application prospects in marine fishery resources exploration and conservation. In this paper, we establish a multi-scale retinex enhancement algorithm and a multi-scale feature-based fish detection model to improve underwater detection accuracy and ensure real-time performance. During image preprocessing, the enhancement algorithm combines the bionic structure of the fish retina and classical retinex theory to filter out underwater environmental noise. The detection model focuses on improving the detection performance on small-size targets using a deep learning method based on a convolutional neural network. We compare our method to current mainstream detection models (Faster R-CNN, RetinaNet, YOLO, SSDetc.), and the proposed model achieves better performance, with a mean Average Precision (mAP) of 78.31% and a mean Miss Rate (mMR) of 54.11% in the open fish image data set. The test results for the data from the field experiment prove the feasibility and stability of our model. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is one of the leading causes of vision loss in adults and is one of the detrimental side effects of the mass prevalence of Diabetes Mellitus (DM). It is crucial to have an efficient screening method for early diagnosis of DR to prevent vision loss. This paper compares and analyzes the various Machine Learning (ML) techniques, from traditional ML to advanced Deep Learning models. We compared and analyzed the efficacy of Convolutional Neural Networks (CNNs), Capsule Networks (CapsNet), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), decision trees, and Random Forests. This paper also considers determining factors in the evaluation, including contrast enhancements, noise reduction, grayscaling, etc We analyze recent research studies and compare methodologies and metrics, including accuracy, precision, sensitivity, and specificity. The findings highlight the advanced performance of Deep Learning (DL) models, with CapsNet achieving a remarkable accuracy of up to 97.98% and a high precision rate, outperforming other traditional ML methods. The Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique substantially enhanced the model's efficiency. Each ML method's computational requirements are also considered. While most advanced deep learning methods performed better according to the metrics, they are more computationally complex, requiring more resources and data input. We also discussed how datasets like MESSIDOR could be more straightforward and contribute to highly evaluated performance and that there is a lack of consistency regarding benchmark datasets across papers in the field. Using the DL models facilitates accurate early detection for DR screening, can potentially reduce vision loss risks, and improves accessibility and cost-efficiency of eye screening. Further research is recommended to extend our findings by building models with public datasets, experimenting with ensembles of DL and traditional ML models, and considering testing high-performing models like CapsNet., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

As a technique in artificial intelligence, a convolution neural network model has been utilized to extract average surface roughness from the geometric characteristics of a membrane image featuring micro- and nanostructures. For surface roughness measurement, e.g. atomic force microscopy and optical profiler, the previous methods have been performed to analyze a porous membrane surface on an interest of region with a few micrometers of the restricted area according to the depth resolution. However, an image from the scanning electron microscope, combined with the feature extraction process, provides clarity on surface roughness for multiple areas with various depth resolutions. Through image preprocessing, the geometric pattern is elucidated by amplifying the disparity in pixel intensity values between the bright and dark regions of the image. The geometric pattern of the binary image and magnitude spectrum confirmed the classification of the surface roughness of images in a categorical scatter plot. A group of cropped images from an original image is used to predict the logarithmic average surface roughness values. The model predicted 4.80 % MAPE for the test dataset. The method of extracting geometric patterns through a feature map-based CNN, combined with a statistical approach, suggests an indirect surface measurement. The process is achieved through a bundle of predicted output data, which helps reduce the randomness error of the structural characteristics. A novel feature extraction approach of CNN with statistical analysis is a valuable method for revealing hidden physical characteristics in surface geometries from irregular pixel patterns in an array of images., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Hyun Wook Kang reports was provided by Chonnam National University. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In the single pruning algorithm, channel pruning or filter pruning is used to compress the deep convolution neural network, and there are still many redundant parameters in the compressed model. Directly pruning the filter will largely cause the loss of key information and affect the accuracy of model classification. To solve these problems, a parallel pruning algorithm combined with image enhancement is proposed. Firstly, in order to improve the generalization ability of the model, a data enhancement method of random erasure is introduced. Secondly, according to the trained batch normalization layer scaling factor, the channels with small contribution are cut off, the model is initially thinned, and then the filters are pruned. By calculating the geometric median of the filters, redundant filters similar to them are found and pruned, and their similarity is measured by calculating the distance between filters. Pruning was done using VGG19 and DenseNet40 on cifar10 and cifar100 data sets. The experimental results show that this algorithm can improve the accuracy of the model, and at the same time, it can compress the calculation and parameters of the model to a certain extent. Finally, this method is applied in practice, and combined with transfer learning, traffic objects are classified and detected on the mobile phone. [ABSTRACT FROM AUTHOR]

This paper presents a robust stereo direct visual odometry method with improved robustness against drastic brightness variation and aggressive rotation. It is achieved by incorporating a direct sparse odometry based on image preprocessing, a depth initialization module, and an abend recovery module into the visual odometry framework. The image preprocessing enhances raw camera images, which facilitates more accurate pixels detecting. Meanwhile, a new error function based on image preprocessing is proposed for making direct visual odometry robust to brightness variation in the environment. In the depth initialization module, the Delaunay triangulation algorithm and feature point matching are combined together for efficient and robust depth estimation. Furthermore, in the abend recovery module, we design a lost/abnormal detection method and a robust state restoration strategy to address the tracking lost/abnormal problem in harsh conditions. Evaluation results on KITTI and EuRoC datasets and a light-switch experiment demonstrate that, with the aid of these three modules, the proposed method can achieve state-of-the-art performance, even compared with visual-inertial fusion methods. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy which impacts the retinal part in the eye because of high sugar degree in the blood. Which reasons retinal damage in the eye leads to complete vision loss. Our intention is to broaden a device as a way to pick out patients with Diabetic Retinopathy using retinal fundus pix. For the diagnosis of diabetic retinopathy, picture pre-processing and characteristic extraction of the diabetic retinal fundus image are performed. [ABSTRACT FROM AUTHOR]

In recent years, there have been frequent jellyfish outbreaks in many offshore areas worldwide, which have severely affected marine fishery production, coastal tourism, coastal industrial cooling water systems, and marine ecology. Achieving the monitoring of jellyfish plays a vital role in solving the problems mentioned above. However, the research on jellyfish is still in the primary stage. Jellyfish detection technology based on deep learning is gradually being applied to jellyfish detection due to its high efficiency and accuracy, but it is not systematic enough and can identify few jellyfish species. So this paper studies a jellyfish detection algorithm based on deep learning. Based on convolution neural network theory and digital image processing technology, 10 species of jellyfish and fish are detected. Because the quality of underwater images affects the detection accuracy, to further improve the accuracy of the detection algorithm, this paper studies the underwater image processing algorithm. Experimental results show that the image quality is better after applying the three algorithms of dark channel prior algorithm, quadratic combining gray world and perfect reflection algorithm, and contrast-limited adaptive histogram equalization algorithm, which is more conducive to detection. Then, deep learning theory is applied to classify jellyfish. By comparing the AlexNet and GoogLeNet backbone networks' classification results, the accuracy of the jellyfish classification task based on the GoogLeNet backbone network is 96.21%, which is better than AlexNet. Finally, the Faster R-CNN algorithm is used to detect jellyfish, and its detection performance is analyzed based on the two backbone networks mentioned above. The results show that the Faster R-CNN algorithm based on GoogLeNet has a higher detection accuracy in the jellyfish detection task, with an average detection accuracy of 74.96%. In addition, we set up a new jellyfish data set, which includes 25,344 images. The images were divided into 11 species, including 10 species of jellyfish and one fish species. The paper's research lays a theoretical and technical foundation for the subsequent construction of a real-time monitoring system for underwater jellyfish optical imaging, plays an important role in the development of jellyfish monitoring technology, and provides valuable information for marine biologists. [ABSTRACT FROM AUTHOR]

Monitoring the growth conditions and behavior of fish will enable scientific management, reduce the threat of losses caused by disease and stress. Traditional monitoring methods are time-consuming, laborious, and untimely monitoring readily leads to aquaculture accidents. As a non-invasive, objective, and repeatable tool, machine vision systems have been widely used in various aspects of aquaculture monitoring. Nevertheless, the complex underwater environment makes it difficult to obtain ideal data processing results only using traditional image processing methods. Due to their powerful feature extraction capabilities, deep learning (DL) algorithms have been widely used in underwater image processing. Hence, the combination of DL algorithms and machine vision for the automated monitoring of aquaculture is of great importance. As evidence for the multidisciplinary aspects of DL applications, attention is focused on the latest DL methods applied to five fields of research: classification, detection, counting, behavior recognition, and biomass estimation. Meanwhile, due to the low training efficiency of DL models caused by insufficient dataset, transfer learning and GAN have also put into spotlight of this filed to pursue high performance of DL models. We also present the challenges and benchmarks in terms of the advantages and disadvantages of the selected method in each field. In addition, we review the sources of image acquisition and pre-processing methods in aquaculture. Finally, the challenges and prospects of DL in aquaculture machine vision systems are discussed. The literature review shows that the deep neural networks such as AlexNet, LSTM, VGG, and GoogLeNet, have been used for aquaculture machine vision systems. [ABSTRACT FROM AUTHOR]

Using effective feature extraction algorithm to accurately represent the texture of iris is the key to iris recognition technology. Based on the particularity of the task of iris recognition, IrisCodeNet, a network model for iris feature coding, is presented in this paper. The network architecture uses an improved BasicBlock combined with a loss function AM-Softmax (additive margin softmax) that can expand the decision boundary. What's more, in order to get the best iris recognition performance, the parameters settings of AM-Softmax, the input forms of iris image preprocessed, the enhancement methods of data and the input sizes of network are studied in detail. The experimental results show that the feature extractor trained by IrisCodeNet is tested on CASIA-Iris-Thousand, CASIA-Iris-Distance and IITD iris databases. The EER (equal error rate) and TAR (true acceptance rate) evaluated are far superior to the used widely traditional algorithms. In particular, IrisCodeNet still achieves excellent recognition results without the steps of traditional iris normalization or precise iris segmentation. The visualization analysis using Grad-CAM (gradient-weighted class activation mapping) algorithm shows that the network framework pays attention to the texture of iris effectively, which proves that IrisCodeNet has a strong ability to extract features of iris. [ABSTRACT FROM AUTHOR]

In this paper, we have discussed in detail about detection and extraction of brain tumor from MRI technique, where the importance of using MRI is also highlighted. Various features extraction methods and classifiers are explained in brain tumor segmentation. This paper mainly focuses on challenges involved in brain tumor analysis, which is helpful for researchers and those who are interested to carry out their research on this topic. [ABSTRACT FROM AUTHOR]

Lung cancer is a form of malignant tumor distinguished by aggressive multiplication of abnormal cells in lung tissues. If we can assure the detection of lung cancer in the early stage, then we have a chance to increase the survival rate by five years as effective treatment is still available at this stage. Many researchers in the field of image processing sector have built various systems to detect cancer by using image processing techniques. Internationally TNM (Tumor, Nodule, Metastases respectively) method is followed by a physician and radiologist to describe the stage of lung cancer. Our proposed system uses image processing techniques to detect and classify the tumor according to the TNM staging method. First, a series of image processing techniques are performed in a Computed tomography (CT) image. Then, features are extracted to identify the region of interest (ROI). In our proposed system, the classification approach is different from the reviewed existing systems, and the detection rate is comparatively high. [ABSTRACT FROM AUTHOR]