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Recognition and extraction of elements from house plans present significant challenges in the construction, decoration and interior design industries. To address this issue, this paper proposes a wall segmentation system for house plans that integrates deep learning and traditional methods. The system comprises several components, such as image preprocessing, main region extraction, wall segmentation and optimisation of wall smoothing. The study combined the rapidity of the traditional method with the robustness of deep learning to enable the extraction of walls from varied image styles and perform smoothing optimisation. The paper demonstrates that the proposed segmentation technique delivers an 89% mean intersection over union, a 94% detection rate and a 96% recognition accuracy. The research surpasses current findings in the same field. Additionally, when combined with the current house map dataset, the system presents a semantic categorisation dataset featuring 6000 images depicting a range of styles, in addition to a recognition dataset including 4000 images. [ABSTRACT FROM AUTHOR]

The nondestructive testing capability of steel surface defects has a significant impact on quality of industrial production. However, the single-stage detection network YOLOv5s model for steel surface defect detection is of insufficient feature extraction ability and susceptibility to noise interference in extracting surface defect images. Accordingly, a steel surface defect detection system based on YOLOv5s-SE-CA model and bi-dimensional empirical mode decomposition (BEMD) image enhancement is proposed. Firstly, the BEMD is used for image threshold denoising to improve the quality of surface defect images and achieve preprocessing of the dataset. Secondly, the squeeze and excitation network (SENet) attention mechanism is introduced into the backbone network of YOLOv5s model to adaptively adjust weights of each feature channel. Meanwhile, the coordinate attention (CA) attention mechanism is integrated to improve network model’s ability to focus on surface defects. Therefore, the YOLOv5s-SE-CA model is designed to enhance detection capability of steel surface defects. Experimental verification was carried out using the NEU-DET noisy dataset with specific evaluation indicators of image enhancement detection system. Results showed the proposed system has superior performance when compared to traditional models trained on noisy and denoising datasets and achieved more accurate detection of surface defects in steel. [ABSTRACT FROM AUTHOR]

Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include deducing the unknown properties of biological structures or tissues from the observed imaging data, presenting a unique challenge in decoding intricate biological phenomena. Regarding disease detection, this technique has played a critical role in optimizing diagnostic efficiency by extracting meaningful insights from different imaging modalities like molecular imaging, MRI, and CT scans. Inverse problems contribute to uncovering subtle abnormalities by employing iterative optimization techniques and sophisticated algorithms, enabling precise and early disease detection. Deep learning (DL) solutions have emerged as robust mechanisms for addressing inverse problems in biomedical image analysis, especially in disease recognition. Inverse problems involve reconstructing unknown structures or parameters from observed data, and the DL model excels in learning complex representations and mappings. This study develops a DL Solution for Inverse Problems in the Advanced Biomedical Image Analysis on Disease Detection (DLSIP-ABIADD) technique. The DLSIP-ABIADD technique exploits the DL approach to solve inverse problems and detect the presence of diseases on biomedical images. To solve the inverse problem, the DLSIP-ABIADD technique uses a direct mapping approach. Bilateral filtering (BF) is used for image preprocessing. Besides, the MobileNetv2 model derives feature vectors from the input images. Moreover, the Henry gas solubility optimization (HGSO) method is applied for optimal hyperparameter selection of the MobileNetv2 model. Furthermore, a bidirectional long short-term memory (BiLSTM) model is deployed to identify diseases in medical images. Extensive simulations have been involved to illustrate the better performance of the DLSIP-ABIADD technique. The experimentation outcomes stated that the DLSIP-ABIADD technique performs better than other models. [ABSTRACT FROM AUTHOR]

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios. [ABSTRACT FROM AUTHOR]

High accuracy in breast cancer classification contributes to the effectiveness of early breast cancer detection. This study aimed to improve the multiview convolutional neural network (MVCNN) performance for classifying breast cancer based on the combined mediolateral (MLO) and craniocaudal (CC) views. The main contribution of this study is the development of a system, consisting of an effective image pre-processing method to create datasets using background removal techniques, and image enhancement. Also, a simplicity of preprocessing stage in the classifier machine, which does not require a feature extraction process. Furthermore, the performance of the classifier was improved by combining preprocessing dataset techniques and evaluating the best hyperparameter in MVCNN architecture. The digital dataset for screening mammography (DDSM) dataset was used for evaluation in this study. The best result from this proposed method achieved accuracy, precision, sensitivity, and specificity of 98.63%, 97.29%, 100%, and 97.29%. The evaluation results demonstrated the capability to improve classification performance. The method proposed in this work can be applied to the detection of breast cancer. [ABSTRACT FROM AUTHOR]

The fundus images of patients with Diabetic Retinopathy (DR) often display numerous lesions scattered across the retina. Current methods typically utilize the entire image for network learning, which has limitations since DR abnormalities are usually localized. Training Convolutional Neural Networks (CNNs) on global images can be challenging due to excessive noise. Therefore, it's crucial to enhance the visibility of important regions and focus the recognition system on them to improve accuracy. This study investigates the task of classifying the severity of diabetic retinopathy in eye fundus images by employing appropriate preprocessing techniques to enhance image quality. We propose a novel two-branch attention-guided convolutional neural network (AG-CNN) with initial image preprocessing to address these issues. The AG-CNN initially establishes overall attention to the entire image with the global branch and then incorporates a local branch to compensate for any lost discriminative cues. We conduct extensive experiments using the APTOS 2019 DR dataset. Our baseline model, DenseNet-121, achieves average accuracy/AUC values of 0.9746/0.995, respectively. Upon integrating the local branch, the AG-CNN improves the average accuracy/AUC to 0.9848/0.998, representing a significant advancement in state-of-the-art performance within the field. [ABSTRACT FROM AUTHOR]

Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis. Leveraging the HAM10000 dataset, a comprehensive collection of dermatoscopic images encompassing a diverse range of skin lesions, this study introduces a sophisticated CNN model tailored for the nuanced task of skin lesion classification. The model's architecture is intricately designed with multiple convolutional, pooling, and dense layers, aimed at capturing the complex visual features of skin lesions. To address the challenge of class imbalance within the dataset, an innovative data augmentation strategy is employed, ensuring a balanced representation of each lesion category during training. Furthermore, this study introduces a CNN model with optimized layer configuration and data augmentation, significantly boosting diagnostic precision in skin cancer detection. The model's learning process is optimized using the Adam optimizer, with parameters fine-tuned over 50 epochs and a batch size of 128 to enhance the model's ability to discern subtle patterns in the image data. A Model Checkpoint callback ensures the preservation of the best model iteration for future use. The proposed model demonstrates an accuracy of 97.78% with a notable precision of 97.9%, recall of 97.9%, and an F2 score of 97.8%, underscoring its potential as a robust tool in the early detection and classification of skin cancer, thereby supporting clinical decision-making and contributing to improved patient outcomes in dermatology. [ABSTRACT FROM AUTHOR]

In response to the current lack of annotations for flower images and insufficient focus on key image features in traditional fine-grained flower image classification based on deep learning, this study proposes the SA-ConvNeXt flower image classification model. Initially, in the image preprocessing stage, a padding algorithm was used to prevent image deformation and loss of detail caused by scaling. Subsequently, the model was integrated using multi-level feature extraction within the Efficient Channel Attention (ECA) mechanism, forming an M-ECA structure to capture channel features at different levels; a pixel attention mechanism was also introduced to filter out irrelevant or noisy information in the images. Following this, a parameter-free attention module (SimAM) was introduced after deep convolution in the ConvNeXt Block to reweight the input features. SANet, which combines M-ECA and pixel attention mechanisms, was employed at the end of the module to further enhance the model's dynamic extraction capability of channel and pixel features. Considering the model's generalization capability, transfer learning was utilized to migrate the pretrained weights of ConvNeXt on the ImageNet dataset to the SA-ConvNeXt model. During training, the Focal Loss function and the Adam optimizer were used to address sample imbalance and reduce gradient fluctuations, thereby enhancing training stability. Finally, the Grad-CAM++ technique was used to generate heatmaps of classification predictions, facilitating the visualization of effective features and deepening the understanding of the model's focus areas. Comparative experiments were conducted on the Oxford Flowers102 flower image dataset. Compared to existing flower image classification technologies, SA-ConvNeXt performed excellently, achieving a high accuracy of 96.7% and a recall rate of 98.2%, with improvements of 4.0% and 3.7%, respectively, compared to the original ConvNeXt. The results demonstrate that SA-ConvNeXt can effectively capture more accurate key features of flower images, providing an effective technical means for flower recognition and classification. [ABSTRACT FROM AUTHOR]

This paper studies a deep learning-based plant detection algorithm. Based on convolution neural network theory and digital image processing technology, 9 species of planes are detected. In order to further improve the accuracy of the detection algorithm, the paper proposes to improve the quality of the images. The experimental results show that the image quality is better after applying the following three algorithms the black channel prioritization algorithm, the quadratic combination algorithm of gray world and perfect reflection, and the contrast-limited adaptive histogram equalization algorithm, which is more conducive to the detection. Then, deep learning theory is applied to classify the plants. Comparing the classification results of AlexNet and GoogLeNet backbone networks, the accuracy of the jellyfish classification task based on GoogLeNet backbone network is 96.21%, which is better than AlexNet. Finally, the Faster R-CNN algorithm is used to detect plants 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 better detection accuracy in the jellyfish detection task, with an average detection accuracy of 74.96%. [ABSTRACT FROM AUTHOR]

In recent times, companies and institutions globally are increasingly adopting automated systems for recording employee attendance due to the inefficiency and error-prone nature of traditional methods. Face recognition is the fastest, most natural, and most accurate way to identify someone, despite its difficulty. Remote deployment and control of the technology using internet of things (IoT) protocols provides real-time attendance data worldwide. We use the Haar-cascade algorithm to detect and extract features and the adaptive boost algorithm confused with convolutional neural network (CNN) algorithm to recognize the face in our proposed smart attendance system. Per frame, the proposed system recognizes multiple faces. Face recognition in 18 conditions was designed into the proposed system to ensure its versatility. The system's graphical user interface (GUI) was made for average users. This work is more important because IoT technology records student attendance and sends data to authorities. We use Raspberry Pi 4 and camera module for our suggested system. Python and OpenCV libraries tested the multiple face image recognition proposal in 18 situations under four conditions. Single-face image recognition was compared to other methods. In most cases, the proposed method was 100% accurate and outperformed related methods. [ABSTRACT FROM AUTHOR]

Abstract Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include deducing the unknown properties of biological structures or tissues from the observed imaging data, presenting a unique challenge in decoding intricate biological phenomena. Regarding disease detection, this technique has played a critical role in optimizing diagnostic efficiency by extracting meaningful insights from different imaging modalities like molecular imaging, MRI, and CT scans. Inverse problems contribute to uncovering subtle abnormalities by employing iterative optimization techniques and sophisticated algorithms, enabling precise and early disease detection. Deep learning (DL) solutions have emerged as robust mechanisms for addressing inverse problems in biomedical image analysis, especially in disease recognition. Inverse problems involve reconstructing unknown structures or parameters from observed data, and the DL model excels in learning complex representations and mappings. This study develops a DL Solution for Inverse Problems in the Advanced Biomedical Image Analysis on Disease Detection (DLSIP-ABIADD) technique. The DLSIP-ABIADD technique exploits the DL approach to solve inverse problems and detect the presence of diseases on biomedical images. To solve the inverse problem, the DLSIP-ABIADD technique uses a direct mapping approach. Bilateral filtering (BF) is used for image preprocessing. Besides, the MobileNetv2 model derives feature vectors from the input images. Moreover, the Henry gas solubility optimization (HGSO) method is applied for optimal hyperparameter selection of the MobileNetv2 model. Furthermore, a bidirectional long short-term memory (BiLSTM) model is deployed to identify diseases in medical images. Extensive simulations have been involved to illustrate the better performance of the DLSIP-ABIADD technique. The experimentation outcomes stated that the DLSIP-ABIADD technique performs better than other models.

Abstract Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis. Leveraging the HAM10000 dataset, a comprehensive collection of dermatoscopic images encompassing a diverse range of skin lesions, this study introduces a sophisticated CNN model tailored for the nuanced task of skin lesion classification. The model’s architecture is intricately designed with multiple convolutional, pooling, and dense layers, aimed at capturing the complex visual features of skin lesions. To address the challenge of class imbalance within the dataset, an innovative data augmentation strategy is employed, ensuring a balanced representation of each lesion category during training. Furthermore, this study introduces a CNN model with optimized layer configuration and data augmentation, significantly boosting diagnostic precision in skin cancer detection. The model’s learning process is optimized using the Adam optimizer, with parameters fine-tuned over 50 epochs and a batch size of 128 to enhance the model’s ability to discern subtle patterns in the image data. A Model Checkpoint callback ensures the preservation of the best model iteration for future use. The proposed model demonstrates an accuracy of 97.78% with a notable precision of 97.9%, recall of 97.9%, and an F2 score of 97.8%, underscoring its potential as a robust tool in the early detection and classification of skin cancer, thereby supporting clinical decision-making and contributing to improved patient outcomes in dermatology.

The fundus images of patients with Diabetic Retinopathy (DR) often display numerous lesions scattered across the retina. Current methods typically utilize the entire image for network learning, which has limitations since DR abnormalities are usually localized. Training Convolutional Neural Networks (CNNs) on global images can be challenging due to excessive noise. Therefore, it's crucial to enhance the visibility of important regions and focus the recognition system on them to improve accuracy. This study investigates the task of classifying the severity of diabetic retinopathy in eye fundus images by employing appropriate preprocessing techniques to enhance image quality. We propose a novel two-branch attention-guided convolutional neural network (AG-CNN) with initial image preprocessing to address these issues. The AG-CNN initially establishes overall attention to the entire image with the global branch and then incorporates a local branch to compensate for any lost discriminative cues. We conduct extensive experiments using the APTOS 2019 DR dataset. Our baseline model, DenseNet-121, achieves average accuracy/AUC values of 0.9746/0.995, respectively. Upon integrating the local branch, the AG-CNN improves the average accuracy/AUC to 0.9848/0.998, representing a significant advancement in state-of-the-art performance within the field.

Abstract Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model’s performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model’s ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes.

Coal and gangue sorting methods based on heavy-medium coal selection technology, jigging technology, flotation technology, dry coal selection technology and γ-ray detection method have high investment costs, low sorting efficiency and serious environmental pollution. The accuracy of the coal gangue sorting method based on CCD cameras is not high, and the X-ray based coal gangue sorting technology can harm the health of personnel. Infrared thermal imaging technology has the advantage of being unaffected by light and dust, and will not cause harm to the human body. A coal gangue recognition method based on infrared thermal imaging has been proposed. Firstly, coal and gangue pass through the heating area under the conveyor belt, and the temperature of the center point of coal and gangue is monitred through an infrared thermal imager to obtain the temperature of the heated coal and gangue. The infrared thermal imager is used to capture the uniformly heated coal and gangue in the heating area, obtaining infrared grayscale and color images of the coal and gangue. Secondly, Gaussian filtering is used to preprocess and extract features from the infrared grayscale images and infrared color images of coal and gangue. The grayscale mean of the infrared grayscale image, the grayscale value feature corresponding to the maximum frequency, and the G-channel first-order moment and G-channel second-order moment features of the infrared color image are used as sorting features. The above four features are used as inputs for the classification model. Finally, support vector machine (SVM) is used for classification and recognition to achieve the goal of recognizing coal and gangue. The experimental results show that the coal gangue recognition method based on infrared thermal imaging has achieved an accuracy rate of over 98% for the sorting of bituminous coal, anthracite, and lignite, and has a good classification effect.

Abstract Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).

Abstract An ionogram image serves as a valuable data for examining the ionospheric bottom side characteristics and variabilities. Spread-F is indicated or identified by plasma irregularity in the ionospheric region. Diffused echo in the ionogram images particularly pose challenges for efficient interpretation required in further applications. An automatic classification of spread-F is presented in this study. Ionogram images are automatically classified using preprocessing techniques to improve the classification performance. In this study, the classification is designed by two machine learning algorithms, including support vector machine (SVM) and convolutional neural network (CNN). The CNN model with preprocessing technique outperforms the SVM alternative based on 4,692 labelled ionogram images from the FMCW-type ionosonde at Chumphon station, Thailand. The model successfully classified clear, frequency spread-F (FSF), range spread-F (RSF), strong spread-F (SSF), and unidentified class with an accuracy of 98.0%, 85.1%, 90.7%, 66.7%, and 99.2%, respectively. The proposed automatic classification models achieved to classify classes of ionogram images. In addition, the image filtering and data preprocessing are useful with ionogram images for improving the model classification performance. Graphical Abstract

At present, most of the parts of mechanical equipment are made of metal, so the accurate detection and extraction of important characteristic parameters of metal parts is the key to determine the quality of mechanical equipment. In order to accurately extract and measure the feature size of metal parts, this research first carried out image preprocessing. Threshold segmentation is performed, and the target traits are extracted from the image to obtain the ROI. Finally, the preprocessed image is extracted from the image by the sub‐pixel edge extraction technology to extract the feature size of the circle to be measured. The difference between the measured value of the characteristic dimension of the spring bearing seat and the gasket and the real value measured by the research method is within the range of 5 and 4 μm, respectively, and both of them meet the requirements of the characteristic dimension accuracy. When the feature size of the two is repeatedly measured, the variation range of the detection results of the spring bearing seat and the gasket is within 9 and 7 μm respectively, and the detection value is much smaller than the feature size tolerance. The detection accuracy of the method can reach 95.394%. The F1 score was 96.029 and the AUC value was 0.93, both higher than other methods. The results show that the use of sub‐pixels on metal parts The edge extraction method is extremely accurate. The research method can effectively improve the detection accuracy of the feature size of metal parts, which is of great significance to the development of the entire metal parts processing industry. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a severe complication of diabetes, causing damage to retinal blood vessels due to high blood sugar levels. Early detection is crucial but often requires significant time and expertise from ophthalmologists. While artificial intelligence (AI) and image recognition hold promise for DR detection, inconsistent image quality poses a challenge. Our study presents a novel technique that integrates pixel color amplification and EfficientNetV2 to enhance fundus image attributes, aiming to address issues related to image quality and achieving superior performance in DR detection. Leveraging EfficientNetV2, an advanced convolutional neural network (CNN) architecture, we achieve 84% multiclass accuracy and 99% binary accuracy, surpassing various other CNN models, including VGG16-fc1, VGG16-fc2, NASNet, Xception, Inception ResNetV2, EfficientNet, InceptionV3, MobileNet, and ResNet50. Our research tackles the critical challenge of early detection of DR, essential for preventing vision loss. This advancement holds the potential to enhance the efficiency and accuracy of DR classification, potentially alleviating the burden on medical professionals and ultimately improving the quality of life for individuals at risk of vision loss. [ABSTRACT FROM AUTHOR]

Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model's performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model's ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes. [ABSTRACT FROM AUTHOR]

The preparation of raw images for subsequent analysis, known as image preprocessing, is a crucial step that can boost the performance of an image classification model. Although deep learning has succeeded in image classification without handcrafted features, certain studies underscore the continued significance of image preprocessing for enhanced performance during the training process. Nonetheless, this task is often demanding and requires high-quality images to effectively train a classification model. The quality of training images, along with other factors, impacts the classification model's performance and insufficient image quality can lead to suboptimal classification performance. On the other hand, achieving high-quality training images requires effective image preprocessing techniques. In this study, we perform exploratory experiments aimed at improving a classification model of unexposed potsherd cavities images via image preprocessing pipelines. These pipelines are evaluated on two distinct image sets: a laboratory-made, experimental image set that contains archaeological images with controlled lighting and background conditions, and a Jōmon–Yayoi image set that contains images of real-world potteries from the Jōmon period through the Yayoi period with varying conditions. The best accuracy performances obtained on the experimental images and the more challenging Jōmon–Yayoi images are 90.48% and 78.13%, respectively. The comprehensive analysis and experimentation conducted in this study demonstrate a noteworthy enhancement in performance metrics compared to the established baseline benchmark. [ABSTRACT FROM AUTHOR]

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2). [ABSTRACT FROM AUTHOR]

An ionogram image serves as a valuable data for examining the ionospheric bottom side characteristics and variabilities. Spread-F is indicated or identified by plasma irregularity in the ionospheric region. Diffused echo in the ionogram images particularly pose challenges for efficient interpretation required in further applications. An automatic classification of spread-F is presented in this study. Ionogram images are automatically classified using preprocessing techniques to improve the classification performance. In this study, the classification is designed by two machine learning algorithms, including support vector machine (SVM) and convolutional neural network (CNN). The CNN model with preprocessing technique outperforms the SVM alternative based on 4,692 labelled ionogram images from the FMCW-type ionosonde at Chumphon station, Thailand. The model successfully classified clear, frequency spread-F (FSF), range spread-F (RSF), strong spread-F (SSF), and unidentified class with an accuracy of 98.0%, 85.1%, 90.7%, 66.7%, and 99.2%, respectively. The proposed automatic classification models achieved to classify classes of ionogram images. In addition, the image filtering and data preprocessing are useful with ionogram images for improving the model classification performance. [ABSTRACT FROM AUTHOR]

Detecting surface defects in strip steel is significantly important for improving production efficiency and product quality. Herein, a novel surface defect detection of strip steel method based on You Only Look Once (YOLO)v5s‐GC is proposed. First, a ResNet‐Mini is developed to preclassify the original dataset to reduce the number of calculations. Subsequently, image preprocessing is conducted to enhance the defect features, which consists of two steps: the first is combining the ResNet‐Mini network weights with Grad‐CAM to crop defective areas and remove background interference and the second is applying the OTSU and normal distribution enhancement algorithm to extract the feature grayscale. Furthermore, a size enhancement strategy is adopted to obtain larger data sizes that could simulate an actual application scenario with a large‐area saccade of strip steel. Finally, the cross‐stage partial module of YOLOv5s is replaced with GhostBottleneck, and the convolutional block attention module is added to the detection neck. The average accuracy of YOLOv5s‐GC for detecting six types of strip steel is 82.4%, marking an increase of 7.0% over YOLOv5s. In addition, the calculation amount of YOLOv5s‐GC is reduced by 48%, and the inference speed is increased by 24%. [ABSTRACT FROM AUTHOR]

Traditional preprocessing methods have problems of low stability and poor universality. To solve these problems, this article conducted effective research on parameter tuning of image preprocessing methods based on the Ant Colony Particle Swarm Optimization (ACPSO) algorithm. This article used peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) as fitness functions. The particle dimension is defined as 2, which represents the size of the median filter and the size of the histogram equalization window. After the iteration, the results of ant colony algorithm and particle swarm optimization (PSO) algorithm were compared, and the parameter with the highest fitness function value was selected as the final preprocessing parameter. The image was preprocessed using the determined optimal parameters. The results showed that the average PSNR and SSIM values of the ACPSO preprocessed images were 5.58 and 0.08 higher than those of traditional preprocessing methods, and the subjective visual evaluation score was also higher. The Otsu's binarization method was used for segmentation. The method of feature extraction using Histogram of Orientated Gradients (HOG) and Local Binary Patterns (LBP), as well as the recognition model using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), was more stable than traditional preprocessing methods. On the other two datasets, the recognition results of ACPSO preprocessed images performed better and showed better universality compared to traditional preprocessing methods. ACPSO algorithm, as a hybrid swarm intelligence algorithm, can effectively play an effective role in optical pattern recognition image preprocessing, solving the problems of low stability and poor universality of traditional preprocessing methods. [ABSTRACT FROM AUTHOR]

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This paper discusses the method of measuring and analyzing the parameters of the retina with subsequent diagnosis based on them of pathological changes due to diabetic retinopathy, which is crucial in the field of medicine to help doctors in timely detection and treatment of the disease. The main problem of biomedical image data analysis is insufficient pre-processing of images for further clear determination of informative indicators. This paper explores the application of machine learning and image processing techniques to develop an effective method for the diagnosis of diabetic retinopathy. The main focus is on obtaining the optimal model using machine learning and different types of neural networks. This paper considered and analyzed such methods of image preprocessing as: median filtering, grayscale conversion, cropping of non-informative areas of the image, selection of contours. The classification results of three rules (Classical Neural Networks (CNNs), Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) were analyzed, and through experimental studies it was determined that the ANN performed the task best (accuracy=87.1 %, reliability=84.6 %, sensitivity=91.6 %, specificity=84 %). An information model was obtained to support decision-making in assessing the condition of the retina using the processing of the obtained microscopic images and further analysis of informative parameters, and a database of more than 35,000 samples and informative features of the retina was formed. Given the sufficient quality of classification and the availability of software and hardware, this method can be developed and applied in practice in medical institutions after conducting all the necessary clinical studies [ABSTRACT FROM AUTHOR]

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.

Abstract In recent times, there has been a proactive effort by various institutions and organizations to preserve historic manuscripts as repositories of traditional knowledge and cultural heritage. Leveraging digital media and emerging technologies has proven to be an efficient way to safeguard these invaluable documents. Such technologies not only facilitate the extraction of knowledge from historic manuscripts but also hold promise for global applications. However, transforming inscribed stone artifacts into binary formats presents significant challenges due to angle distortion, subtle differences between foreground and background, background noise, variations in text size, and related issues. A pivotal aspect of effective image processing in preserving the rich information and wisdom encoded in stone inscriptions lies in employing appropriate pre-processing methods and techniques. This research paper places a special focus on elucidating various preprocessing techniques, encompassing resizing, grayscale conversion, enhancement of brightness and contrast, smoothening, noise removal, morphological operations, and thresholding. To comprehensively assess these techniques, we undertake a study involving stone inscription images extracted from the Tanjore Brihadeeswar Temple, dating back to the eleventh century during the reign of Raja Raja Chola. This choice is informed by the manifold challenges associated with image correction, such as distortion and blurring. We undertake an evaluation encompassing a diverse array of stone background structures, including types like flawless-bright-moderately legible, dark-illegible, flawless-bright-illegible, flawless-dull, flawless-irregular-moderate, highly impaired-dark-legible, highly impaired-irregular-illegible, impaired-dark-moderate, impaired-dull-moderately legible, impaired-dusky dark-moderate, and very impaired-dusky dark-legible. Subsequently, the processed outputs are subjected to character recognition and information extraction, with a focus on comparing the outcomes of various pre-processing methods, including binarization and grayscale conversion. This study seeks to contribute insights into the most effective pre-processing strategies for enhancing the legibility and preservation of ancient Indian script images etched onto diverse stone background structures.

Accurate segmentation of pulmonary nodule within medical imagery is of great significance for classification and diagnosis. This task is profoundly challenging due to scarcity of annotated data, the diversity of lung nodule features, and the complexity of the background, making it very difficult to accurately and automatically segment lung nodules. Fortunately, deep learning is widely used in the field of pulmonary nodule segmentation and has achieved good and promising results. In this study, we conducted a comprehensive review of pulmonary nodule segmentation in scholarly journals and conferences within the domains of engineering and computer science over the past five years. Our focus was on the application of various deep-learning architectures for pulmonary nodule segmentation and the exploration of diverse deep learning models tailored to the unique challenges posed by this task. We summarized the pivotal methodologies underpinning lung nodule segmentation based on deep learning, including data preprocessing, network architecture, and loss function. Additionally, we discussed the advantages and disadvantages of different models. Finally, we identified research gaps and outlined future directions in the field of deep learning-driven pulmonary nodule image segmentation, aiming to provide researchers with a comprehensive evaluation of recent studies.