Knee Osteoarthritis (KOA), the most prevalent joint disease, significantly impacts elderly mobility due to progressive cartilage degeneration. Early prediction is crucial for preventing disease progression and guiding effective treatment plans. This paper proposes an EnsembleTL-ACO, fully automated, computer-aided diagnosis (CAD) system for accurate and rapid KOA severity grading. The proposed CAD system leverages an ensemble transfer learning strategy to extract robust deep features by fusing multiple deep learning models. It combines features from two consecutive AI models: (1) AlexNet for implicit class-wise deep feature extraction from preprocessed data, and (2) a custom IsrNet for further feature depth. Unsupervised k-means clustering based on PCA dimensionality reduction decomposes each class into subgroups, further refining features. Finally, Ant Colony Optimization (ACO) selects the most informative features. Evaluated on the Osteoarthritis Initiative (OAI) dataset, the proposed system achieves high accuracy in classifying the five KOA severity grades. With 1000 optimized features, it reaches average overall accuracies of 89.89

This article presents a detailed overview of a dataset, created for in-depth analysis of soccer events. This dataset will serve as a foundation for researchers and practitioners in the field, providing a perspective on different soccer events under various views. This soccer dataset is designed to categorize soccer matches into various events and contains 187,151 instances divided across 14 groups. To make this dataset simple, it is separated into two main datasets. The first dataset is known as the “View-Based Dataset.” which is divided into four categories: Long view, Medium view, Short view, and Outer view, for a total of 137,196 images. The second dataset is the “Event-Based Dataset,” which has 10 separate classes that highlight multiple soccer events Red card, Spectator, Yellow card, Plenty stock, Player celebration, Offside, Goal attempt, Goal, and Free kick for a total of 38,728 images. Each class in both datasets helps to provide a full understanding of soccer events. This dataset can serve as a foundation for future video analysis studies, promoting progress in soccer analytics and related domains.

[This retracts the article DOI: 10.1016/j.heliyon.2024.e30954.].

VGG-16 and Inception are widely used CNN architectures for image classification, but they face challenges in target categorization. This study introduces B4-GraftingNet, a novel deep learning model that integrates VGG-16's hierarchical feature extraction with Inception's diversified receptive field strategy. The model is trained on the OCT-CXR dataset and evaluated on the NIH-CXR dataset to ensure robust generalization. Unlike conventional approaches, B4-GraftingNet incorporates binary particle swarm optimization (BPSO) for feature selection and grad-CAM for interpretability. Additionally, deep feature extraction is performed, and multiple machine learning classifiers (SVM, KNN, random forest, na & iuml;ve Bayes) are evaluated to determine the optimal feature representation. The model achieves 94.01% accuracy, 94.22% sensitivity, 93.36% specificity, and 95.18% F1-score on OCT-CXR and maintains 87.34% accuracy on NIH-CXR despite not being trained on it. These results highlight the model's superior classification performance, feature adaptability, and potential for real-world deployment in both medical and general image classification tasks.

Finding the correct match to a probe image from a vast amount of data is critical for the online retrieval of apparel images. These images are captured under an uncontrolled environment (e.g., viewpoint and illumination changes); therefore, such type of data is extremely challenging in Content-Based Image Retrieval (CBIR) research. Even in Google searches, most of the time the query results are provided with inaccurate results or duplicate results due to the minor variations between apparel. Another major challenge is that the extracted feature vector dimensions are too high and difficult to handle. In this paper, a method named Multifeature Representation with Maximum Correlation-based Feature Fusion, and Matching (MFR-MCF2M) is proposed for apparel retrieval. This method consists of three modules: (1) Multifeature Representation Module (MFR-M), (2) Maximum Correlation-based Feature Fusion Module (MCF2-M) and (3) Multifeature Matching Module (MFM-M). In the MFR module, the shape, texture and deep features of apparel images are extracted using a Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and a pretrained deep CNN model, respectively. Also, the dimensionality of extracted features is reduced using the proposed Feature Subselection (FSS) method. The MCF module is implemented to measure the maximum correlation between reduced feature vectors. Finally, MCF2 is performed using Euclidean distance and a generated Feature Correlation Vector (FCV) to improve the retrieval accuracy and as the benchmark to assess the efficacy of the proposed method. In addition, a new large-scale dataset named Apparel Images Gallery (AIG), which consists of 130,000 images, has been provided to the community. The performance of the proposed MFR-MCF2M method is evaluated on three datasets, including two publicly available datasets and the proposed AIG dataset. The retrieval results are obtained after passing through the threshold function of both the Euclidean distance and the computed FCV. The proposed method achieved an accuracy of 78.3% on the clothing dataset, 94.8% on the CR dataset and 89.1% on the proposed AIG dataset. Consequently, the MFR-MCF2M outperformed state-of-the-art (SOTA) apparel retrieval methods.

Corn diseases significantly impact crop yields, posing a major challenge to agricultural productivity. Early and accurate detection of these diseases is crucial for effective management and mitigation. Existing methods, mostly relying on analyzing corn leaves, often lack the precision to identify and classify a wide range of diseases under varying conditions. This study introduces a novel approach to detecting corn diseases using image processing and deep learning techniques, aiming to enhance detection accuracy through pre-processing, improved feature extraction and selection, and classification algorithms. A new deep Convolutional Neural Network (CNN) model named TreeNet, with 35 layers and 38 connections, is proposed. TreeNet is pre-trained using the Plant Village imaging dataset. For image pre-processing, the YCbCr color space is utilized to improve color representation and contrast. Feature extraction is performed using TreeNet and two pre-trained models, Darknet-53, and DenseNet-201, with features fused using a serial-based fusion method. The Entropy-coded Sine Cosine Algorithm is applied for feature selection, optimizing the feature set for classification. The selected features are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers, with extensive experiments conducted using both 5-fold and 10-fold cross-validation, and feature sizes ranging from 200 to 1150. The proposed method achieves classification accuracy, precision, recall, and F1-score of 99.8%, 99%, 100%, and 99%, respectively, surpassing existing benchmarks. The integration of TreeNet with Darknet-53 and DenseNet-201, along with robust pre-processing and feature selection, significantly improves corn disease detection, highlighting the potential of advanced CNN architectures in agriculture.

The computer vision field has wide applications in various areas, including sports. Almost all sports events have been exploiting the best features. Sports videos are structure-based, and due to this characteristic, these videos can be categorized into interesting and non-interesting events. Identifying the view of the video and separating important events from non-interesting events is challenging. However, correct view detection can lead to correct event detection. Various researchers have proposed many strategies for detecting events in sports videos. Significant research shows some gaps while generating highlights due to limited work available in the long or short view; there is still a need for some automated methods. The main purpose of this research work is to detect key events by normalizing the data, extracting features, fusing those features, and classifying them. In this research, events are detected by classification. A new dataset is created for research purposes. The benchmark dataset is divided into two subsets, which can be used separately or as part of a larger dataset. A proposed novel approach for event highlight generation in long and short view is presented using a fusion of AlexNet and VGGNet architectures to explore the model's efficiency in the context of accurate highlight generation deep learning models fused with handcrafted. AlexNet and VGG16 pre-trained deep CNN models are applied to extract deep prior features, which are then combined with HOG to improve the results. The proposed methodology undergoes evaluation on a dataset that we created, resulting in an accuracy of 99.6%.

Complications in diabetes lead to diabetic retinopathy (DR) hence affecting the vision. Computerized methods performed a significant role in DR detection at the initial phase to cure vision loss. Therefore, a method is proposed in this study that consists of three models for localization, segmentation, and classification. A novel technique is designed with the combination of pre-trained ResNet-18 and YOLOv8 models based on the selection of optimum layers for the localization of DR lesions. The localized images are passed to the designed semantic segmentation model on selected layers and trained on optimized learning hyperparameters. The segmentation model performance is evaluated on the Grand-challenge IDRID segmentation dataset. The achieved results are computed in terms of mean IoU 0.95,0.94, 0.96, 0.94, and 0.95 on OD, SoftExs, HardExs, HAE, and MAs respectively. Another classification model is developed in which deep features are derived from the pre-trained Efficientnet-b0 model and optimized using a Genetic algorithm (GA) based on the selected parameters for grading of NPDR lesions. The proposed model achieved greater than 98% accuracy which is superior to previous methods.

In this manuscript, imbalanced and small sample space (IB-SSS) dataset problems for pedestrian gender classification using fusion of selected deep and traditional features (PGC-FSDTF) are considered. In this regard, data preparation is first done through data augmentation and preprocessing steps to handle imbalanced classification problem and environmental effects, respectively. The proposed approach follows different types of feature extraction schemes, for instance, pyramid histogram of oriented gradients, hue saturation value histogram, deep visual features of DenseNet201 and InceptionResNetV2-based convolutional neural network architectures. The parallel fusion method computes the maximum and average values-based features from the learned features of both deep networks. Features are selected through features selection methods such as entropy and principal component analysis (PCA). The subsets of features are serially fused and provided to multiple classifiers to perform gender classification on IB-SSS datasets. Resultantly, the proposed PGC-FSDTF method shows better results in terms of different accuracies (overall, mean, and balanced), and area under curve on selected datasets. Further, improved results are achieved on applied datasets using PCA-based selected features and medium Gaussian support vector machine (M-SVM) classifier. These results on different datasets confirm that the selected feature combination provides a way to handle IB-SSS issues for PGC effectively.

MRI segmentation and analysis are significant tasks in clinical cardiac computations. A cardiovascular MR scan with left ventricular segmentation seems necessary to diagnose and further treat the disease. The proposed method for left ventricle segmentation works as a combination of the intelligent histogram-based image enhancement technique with a Light U-Net model. This technique serves as the basis for choosing the low-contrast image subjected to the stretching technique and produces sharp object contours with good contrast settings for the segmentation process. After enhancement, the images are subjected to the encoder–decoder configuration of U-Net using a novel lightweight processing model. Encoder sampling is supported by a block of three parallel convolutional layers with supporting functions that improve the semantics for segmentation at various levels of resolutions and features. The proposed method finally increased segmentation efficiency, extracting the most relevant image resources from depth-to-depth convolutions, filtering them through each network block, and producing more precise resource maps. The dataset of MICCAI 2009 served as an assessment tool of the proposed methodology and provides a dice coefficient value of 97.7%, accuracy of 92%, and precision of 98.17%.

Pedestrian gender classification (PGC) is a key task in full-body-based pedestrian image analysis and has become an important area in applications like content-based image retrieval, visual surveillance, smart city, and demographic collection. In the last decade, convolutional neural networks (CNN) have appeared with great potential and with reliable choices for vision tasks, such as object classification, recognition, detection, etc. But CNN has a limited local receptive field that prevents them from learning information about the global context. In contrast, a vision transformer (ViT) is a better alternative to CNN because it utilizes a self-attention mechanism to attend to a different patch of an input image. In this work, generic and effective modules such as locality self-attention (LSA), and shifted patch tokenization (SPT)-based vision transformer model are explored for the PGC task. With the use of these modules in ViT, it is successfully able to learn from stretch even on small-size (SS) datasets and overcome the lack of locality inductive bias. Through extensive experimentation, we found that the proposed ViT model produced better results in terms of overall and mean accuracies. The better results confirm that ViT outperformed state-of-the-art (SOTA) PGC methods.

Medical applications are increasing day by day but the necessity for more autonomous applications is always a challenge in the real world.The human brain works autonomously and takes decisions, so developing applications which work like the human brain is a key step to the autonomous medical world.In this special issue, the focus is to perform computing in medical applications in the way a human brain does.This can be achieved using artificial neural networks and different algorithms of artificial intelligence along with classification and segmentation.Deep learning and machine learning algorithms performed brain-like computing in medical applications with improved accuracy.Smart healthcare system-a brain-like computing approach for analyzing the performance of detectron2 and PoseNet models for anomalous action detection in aged people with movement impairments by Divya, R. and J.D. Peter performed object detection and recognition of abnormal

Human gait recognition (HGR) shows high importance in the area of video surveillance due to remote access and security threats. HGR is a technique commonly used for the identification of human style in daily life. However, many typical situations like change of clothes condition and variation in view angles degrade the system performance. Lately, different machine learning (ML) techniques have been introduced for video surveillance which gives promising results among which deep learning (DL) shows best performance in complex scenarios. In this article, an integrated framework is proposed for HGR using deep neural network and fuzzy entropy controlled skewness (FEcS) approach. The proposed technique works in two phases: In the first phase, deep convolutional neural network (DCNN) features are extracted by pre‐trained CNN models (VGG19 and AlexNet) and their information is mixed by parallel fusion approach. In the second phase, entropy and skewness vectors are calculated from fused feature vector (FV) to select best subsets of features by suggested FEcS approach. The best subsets of picked features are finally fed to multiple classifiers and finest one is chosen on the basis of accuracy value. The experiments were carried out on four well‐known datasets, namely, AVAMVG gait, CASIA A, B and C. The achieved accuracy of each dataset was 99.8, 99.7, 93.3 and 92.2%, respectively. Therefore, the obtained overall recognition results lead to conclude that the proposed system is very promising.

Automated analysis of the gastric lesions in endoscopy videos is a challenging task and dynamics of the gastrointestinal environment make it even more difficult. In computer-aided diagnosis, gastric images are analyzed by visual descriptors. Various Deep Convolutional Neural Network (DCNN) models are available for representation learning and classification. In this paper, a computer aided diagnosis system is presented for the classification of abnormalities in Videos Endoscopy (VE) images based on Deep Gray-Level Co-occurrence Matrix (DeepGLCM) texture features. In our scheme, the convolutional layers of an already trained model are employed for acquisition of the statistical features from responses of filters to estimate the texture representation of VE frames. A learning model is trained on these features for gastric frames classification. The results obtained by using public datasets of endoscopy images to calculate the performance of the proposed method. In addition, we also use a private endoscopy dataset which is acquired from the University of Aveiro. The DeepGLCM outperforms by achieving the average accuracy of approximate to 92% and 0.96 area under the curve (AUC) for the chromoendoscopy (CH) dataset and approximate to 85% accuracy for Confocal Laser Endomicroscopy (CLE) and white light video endoscopy datasets. It is evident that the DeepGLCM texture features provide a better representation than the traditional texture extraction methods by efficiently dealing with variance in images due to different imaging technologies.

Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.

Due to rapid development in Artificial Intelligence (AI) and Deep Learning (DL), it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling. Such technique is sensitive to these models. Thus, fake samples cause AI and DL model to produce diverse results. Adversarial attacks that successfully implemented in real world scenarios highlight their applicability even further. In this regard, minor modifications of input images cause ???Adversarial Attacks??? that altered the performance of competing attacks dramatically. Recently, such attacks and defensive strategies are gaining lot of attention by the machine learning and security researchers. Doctors use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy (WCE). However, using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding abnormality. As a result, it took weeks to generate patients test report, which is tiring and strenuous for them. Therefore, researchers come out with the solution to adopt computerized technologies, which are more suitable for the classification and detection of such abnormalities. As far as the classification is concern, the adversarial attacks generate problems in classified images. Now days, to handle this issue machine learning is mainstream defensive approach against adversarial attacks. Hence, this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method (FGSM) and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid attacks. Results obtained on the WCE images which are vulnerable to adversarial attack are 96.30% accurate and prove that the proposed defensive model is robust when compared to competitive existing methods.

The online retrieval of clothes-related images is crucial because finding the exact items, like the query image from a large amount of data, is highly challenging. However, significant clothes image variations degrade visual search retrieval accuracy. Another problem with retrieval accuracy is the high dimensions of feature vectors obtained from pre-trained deep CNN models. This research aims to enhance clothes retrieval training and test accuracy by using two different means. Initially, features are extracted using the modified AlexNet (M-AlexNet) with slight modification. The ReLU activation function is replaced with a self-regularized Mish activation function because of its non-monotonic nature. The M-AlexNet with Mish is trained on the CIFAR-10 dataset using the SoftMax classifier. Another contribution is to reduce the dimensions of feature vectors obtained from M-AlexNet. The dimensions of features are reduced by selecting the top $k$ -ranked features and removing some of the different features using the proposed Joint Shannon’s Entropy Pearson Correlation Coefficient (JSE-PCC) technique to enhance the clothes retrieval performance. To calculate the efficacy of suggested methods, the comparison is performed with other deep CNN models such as baseline AlexNet, VGG-16, VGG-19, and ResNet50 on DeepFashion2, MVC, and the proposed Clothes Image Dataset (CID). Extensive experiments indicate that AlexNet with Mish attains 85.15%, 82.04%, and 83.65% accuracy on DeepFashion2, MVC, and 83.65% on CID datasets. Hence, M-AlexNet and the proposed feature selection technique surpassed the results with a margin of 5.11% on DeepFashion2, 1.95% on MVC, and 3.51% on CID datasets.

Visual analysis of fashion images gain much attention in the fashion industry due to its commercial and social importance. In recent years, deep learning techniques offer overwhelming progress in improving the accuracy of fine‐grained apparel segmentation with accurate bounding box prediction. The baseline pixel‐based masking techniques show excellent performance in object detection and segmentation but sometimes ignores the boundary of objects, resulting in uneven and complicated segmentation masks. Moreover, it is time taking to generate a multi‐scale feature map against each anchor box. To remedy this problem, a more accurate, faster, and suitable deep learning architecture is proposed that accurately detects, classify, and performs fine‐grained segmentation of cloth products in a single platform. In this paper, initially, an Object Class Head Detector model is proposed in which the baseline Mask‐RCNN model is used as a reference model. Here, we replace the Region Proposal Network with the proposed modified YoloV2 model to locate apparel products with its class prediction. The modified YoloV2 model has more capability to detect tiny objects because of local and high‐level feature fusion. The goal of this step is to accurately locate the objects in minimum time intervals. Furthermore, the predicted bounding box is converted to object shape offsets using deep snake architecture that tightly fits onto the apparel shape. It can improve the accuracy of cloth shape segmentation by preserving object contours. The proposed architecture is empirically validated on various existing fashion image datasets. The experimental results illustrate that the proposed architecture performs better on the Deepfashion2 dataset with mAP of 86.86%, as compared to other state‐of‐the‐art deep learning models.

The fracture of the elbow is common in human beings. The complex structure of the elbow, including its irregular shape, border, etc., makes it difficult to correctly recognize elbow fractures. To address such challenges, a method is proposed in this work that consists of two phases. In Phase I, pre-processing is performed, in which images are converted into RGB. In Phase II, pre-trained convolutional models Darknet-53 and Xception are used for deep feature extraction. The handcrafted features, such as the histogram of oriented gradient (HOG) and local binary pattern (LBP), are also extracted from the input images. A principal component analysis (PCA) is used for best feature selection and is serially merged into a single-feature vector having the length of N×2125. Furthermore, informative features N×1049 are selected out of N×2125 features using the whale optimization approach (WOA) and supplied to SVM, KNN, and wide neural network (WNN) classifiers. The proposed method’s performance is evaluated on 16,984 elbow X-ray radiographs that are taken from the publicly available musculoskeletal radiology (MURA) dataset. The proposed technique provides 97.1% accuracy and a kappa score of 0.943% for the classification of elbow fractures. The obtained results are compared to the most recently published approaches on the same benchmark datasets.