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Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images' quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image. [ABSTRACT FROM AUTHOR]

Rice is the most widely consumed grain across the world. The rice plants often suffer from diseases. Early detection of such diseases and adopting remedial measures can help the farmers to avoid major losses and can produce best-quality crops in large quantities. However, the conventional rice leaf disease detection techniques are often not accurate, time-consuming and sometimes require laboratory testing. In this context, automatic rice leaf disease detection techniques are presented based on the various deep learning classifiers (namely MobileNetV2, ResNet50, VGG16 and Le-Net5) and an Android application is also developed in order to instantly determine the possible rice diseases from the uploaded rice leaf images captured by the smartphone. The developed models are tested using the publicly available benchmark rice leaf dataset containing three types of rice leaf diseases, namely bacterial leaf blight, leaf smut and brown spot. Experimental results show that MobileNetV2 model performed better compared to other models in terms of classification accuracy, recall and F1-score. The results of statistical significance test also confirmed the superiority of the MobileNetV2 model over other compared deep learning models. [ABSTRACT FROM AUTHOR]

Melanoma is an acute and chronic skin disease, and if left untreated, it increases morbidity in the patient. The high degree of similarity between the images of different classes and the complex structure of lesion images makes automated skin lesion diagnosis very challenging. Dermoscopic images are widely used for the diagnosis of skin lesions. This study proposes a novel method by analyzing deep features and wavelet features. In this regard, a standard pre-trained ResNet50 model is used for extracting deep features. Lesion images are transformed to wavelet domain using lifting wavelet transform (LWT). The level-2 approximation component of LWT is taken as wavelet feature. Deep features and wavelet features are fused, and the neighborhood component analysis (NCA) algorithm is subsequently used to select a subset of fused features with reduced dimensions. The NCA-reduced feature set is classified by a multilayer perceptron (MLP). The suggested method is validated on the publicly available ISIC 2016 challenge dataset and PH2 dataset. An accuracy of 98%, auc of 99.62% on PH2 dataset while 71% accuracy, 80.61% auc on ISIC 2016 dataset is obtained. The experimental results are comparable to the existing state-of-the-art methods. This study demonstrates that the integration of LWT features improves discriminative information. Feature reduction by using NCA is able to provide robust details with reduced noise and redundancy. [ABSTRACT FROM AUTHOR]

Students' behavioral and emotional engagement in the classroom environment may reflect the students' learning experience and subsequent educational outcomes. The existing research has overlooked the measurement of behavioral and emotional engagement in an offline classroom environment with more students, and it has not measured the student engagement level in an objective sense. This work aims to address the limitations of the existing research and presents an effective approach to measure students' behavioral and emotional engagement and the student engagement level in an offline classroom environment during a lecture. More precisely, video data of 100 students during lectures in different offline classes were recorded and pre-processed to extract frames with individual students. For classification, convolutional-neural-network- and transfer-learning-based models including ResNet50, VGG16, and Inception V3 were trained, validated, and tested. First, behavioral engagement was computed using salient features, for which the self-trained CNN classifier outperformed with a 97%, 91%, and 83% training, validation, and testing accuracy, respectively. Subsequently, the emotional engagement of the behaviorally engaged students was computed, for which the ResNet50 model surpassed the others with a 95%, 90%, and 82% training, validation, and testing accuracy, respectively. Finally, a novel student engagement level metric is proposed that incorporates behavioral and emotional engagement. The proposed approach may provide support for improving students' learning in an offline classroom environment and devising effective pedagogical policies. [ABSTRACT FROM AUTHOR]

Pneumonia is a condition characterised by the sudden inflammation of lung tissue, which is triggered by microorganisms such as fungi, viruses, and bacteria. Chest X-ray imaging (CXR) can detect pneumonia, but it requires considerable time and medical expertise. Consequently, the objective of this study is to diagnose pneumonia using CXR imaging in order to effectively detect early cases of pneumonitis in children. The study employs the ensemble transfer learning convolutional neural network (ETL-CNN) transfer learning ensemble, which combines multiple CNN transfer learning models. Resnet50-VGG19 and VGG19-Xception are the ETL-CNN models used in this investigation. Comparing ETL-CNN models to CNN transfer learning models such as Resnet50, VGG19, and Xception. Pediatric CXR pneumonia, which consists of a normal and pneumonia image, is the source of these study results. The results of this analysis indicate that Resnet50-VGG19 achieved the highest level of accuracy, 99.14%. Additionally, the Resnet50-VGG19 obtained the highest levels of precision and recall when comparing to other models. Consequently, the conclusion of this study is that the Resnet50-VGG19 model can generate acceptable classification performance for pediatric pneumonia based on CXR. This study improves classification results for performance when compared to earlier studies. [ABSTRACT FROM AUTHOR]

One of the most common issues in manufacturing is the inability to persistently maintain good quality, which can lead to product defects and customer complaints. In this research, the novel implementation of deep learning for fabric defect classification in FastAI was proposed. The residual network structure of ResNet50 was trained through transfer learning to classify the data set that contained five classes of fabric images: good, burned, frayed, ripped, and stained. A novel approach to constructing the data set was undertaken by compiling randomly downloaded fabric images within the aforementioned five classes with a broad variety from the internet. The effect of the two splitting methods in dividing the data into training and validation data was investigated. Random splitting divides the data into random class proportions, while stratified splitting maintains the original class proportions. Models were tested offline with unseen data and reached a mean accuracy of 92.5% for the 2-class model and 70.3% for the 5-class model. Based on the attained accuracy and precision, no splitting method was superior to the other. The feasibility of the system's online implementation was evaluated by integrating a smartphone camera to capture and classify fabric samples, with a mean accuracy of 75.6% for the 5-class model. [ABSTRACT FROM AUTHOR]

Fabric defects in the conventional manufacturing of acoustic panels are detected via manual visual inspections, which are prone to problems due to human errors. Implementing an automated fabric inspection system can improve productivity and increase product quality. In this work, advanced machine learning (ML) techniques for fabric defect detection are reviewed, and two deep learning (DL) models are developed using transfer learning based on pre-trained convolutional neural network (CNN) architectures. The dataset used for this work consists of 1800 images with six different classes, made up of one class of fabric in good condition and five classes of fabric defects. The model design process involves pre-processing of the images, modification of the neural network layers, as well as selection and optimisation of the network's hyperparameters. The average accuracies of the two CNN models developed in this work, which used the GoogLeNet and the ResNet50 architectures, are 89.84% and 95.45%, respectively, showing statistically significant results. The interpretability of the models is discussed using the Grad-CAM technique. Relevant image acquisition hardware requirements are also put forward for integration with the detection software, which can enable successful deployment of the model for the automated fabric inspection. [ABSTRACT FROM AUTHOR]

Breast cancer has become a major worldwide health issue, accounting for a large portion of the mortality rate among women. As a result, the need for early detection techniques to enhance prognosis is increasing. Many techniques are being used to detect breast cancer early, and treatment outcomes are frequently favorable when the disease is detected early. Mammography is a commonly used and very successful method for identifying breast cancer among these modalities. Through additional image processing operations like resizing and normalizing, this technology allows the detection of malignant spots from mammography pictures of the affected area. The goal of our research is to improve breast cancer detection and diagnosis speed and accuracy. In this study, we investigate the use of deep learning methods, particularly the visual geometry group (VGG16) and ResNet50 models, for mammography-based breast cancer detection. We assess the performance of the VGG16 and ResNet50 models by training and testing on a mammogram dataset that consists of 322 images from the mammographic image analysis society (MIAS) dataset. The suggested models aim to classify these images into normal, benign, and malignant groupings. Our results show better performance when compared to existing approaches. The proposed methods VGG16 and ResNet50 show promising results, achieving a classification accuracy of 91.23% and 99.01% respectively. [ABSTRACT FROM AUTHOR]

Background: Intracranial neoplasm, often referred to as a brain tumor, is an abnormal growth or mass of tissues in the brain. The complexity of the brain and the associated diagnostic delays cause significant stress for patients. This study aims to enhance the efficiency of MRI analysis for brain tumors using deep transfer learning. Methods: We developed and evaluated the performance of five pre-trained deep learning models—ResNet50, Xception, EfficientNetV2-S, ResNet152V2, and VGG16—using a publicly available MRI scan dataset to classify images as glioma, meningioma, pituitary, or no tumor. Various classification metrics were used for evaluation. Results: Our findings indicate that these models can improve the accuracy of MRI analysis for brain tumor classification, with the Xception model achieving the highest performance with a test F1 score of 0.9817, followed by EfficientNetV2-S with a test F1 score of 0.9629. Conclusions: Implementing pre-trained deep learning models can enhance MRI accuracy for detecting brain tumors. [ABSTRACT FROM AUTHOR]

The detection of polyps plays a significant role in colonoscopy examinations, cancer diagnosis, and early patient treatment. However, due to the diversity in the size, color, and shape of polyps, as well as the presence of low image contrast with the surrounding mucosa and fuzzy boundaries, precise polyp segmentation remains a challenging task. Furthermore, this task requires excellent real‐time performance to promptly and efficiently present predictive results to doctors during colonoscopy examinations. To address these challenges, a novel neural network, called CafeNet, is proposed in this paper for rapid and accurate polyp segmentation. CafeNet utilizes newly designed multi‐scale context aggregation (MCA) modules to adapt to the extensive variations in polyp morphology, covering small to large polyps by fusing simplified global contextual information and local information at different scales. Additionally, the proposed network utilizes newly designed multi‐level foreground enhancement (MFE) modules to compute and extract differential features between adjacent layers and uses the prediction output from the adjacent lower‐layer decoder as a guidance map to enhance the polyp information extracted by the upper‐layer encoder, thereby improving the contrast between polyps and the background. The polyp segmentation performance of the proposed CafeNet network is evaluated on five benchmark public datasets using six evaluation metrics. Experimental results indicate that CafeNet outperforms the state‐of‐the‐art networks, while also exhibiting the least parameter count along with excellent real‐time operational speed. [ABSTRACT FROM AUTHOR]

In edge computing, optimizing deep neural networks within limited computational resources is essential. This study concentrates on improving the efficacy of the ResNet50 model via static quantization. The applications targeted in this investigation encompass robotic space exploration, specifically Martian terrain classification and wildfire detection scenarios. We conducted performance evaluations on multiple platforms, including a desktop PC, an Intel Next Unit of Computing mounted on a drone, and an Nvidia Jetson Nano integrated into a custom robot, to assess the impact of quantization on computational efficiency and model accuracy. Our findings demonstrate that quantization achieved a reduction in model size by approximately 73-74% and decreased average inference times by 56-68%, with minimal effect on accuracy. These results corroborate the utility of quantization as a viable approach for the deployment of complex neural networks in edge computing environments, ensuring the retention of high accuracy levels. [ABSTRACT FROM AUTHOR]

Copyright of Arquivos Brasileiros de Oftalmologia is the property of Arquivos Brasileiros de Oftalmologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Smart contracts are programs that reside and execute on a blockchain, like any transaction. They are automatically executed when preprogrammed terms and conditions are met. Although the smart contract (SC) must be presented in the blockchain for the integrity of data and transactions stored within it, it is highly exposed to several vulnerabilities attackers exploit to access the data. In this paper, classification and detection of vulnerabilities targeting smart contracts are performed using deep learning algorithms over two datasets containing 12,253 smart contracts. These contracts are converted into RGB and Grayscale images and then inserted into Residual Network (ResNet50), Visual Geometry Group-19 (VGG19), Dense Convolutional Network (DenseNet201), k-nearest Neighbors (KNN), and Random Forest (RF) algorithms for binary and multi-label classification. A comprehensive analysis is conducted to detect and classify vulnerabilities using different performance metrics. The performance of these algorithms was outstanding, accurately classifying vulnerabilities with high F1 scores and accuracy rates. For binary classification, RF emerged in RGB images as the best algorithm based on the highest F1 score of 86.66% and accuracy of 86.66%. Moving on to multi-label classification, VGG19 stood out in RGB images as the standout algorithm, achieving an impressive accuracy of 89.14% and an F1 score of 85.87%. To the best of our knowledge, and according to the available literature, this study is the first to investigate binary classification of vulnerabilities targeting Ethereum smart contracts, and the experimental results of the proposed methodology for multi-label vulnerability classification outperform existing literature. [ABSTRACT FROM AUTHOR]

Rationale and objectivesDiagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed by ResNet50 to estimate the malignancy probability.Materials and methodsTwo datasets were used. The first set had 176 cases, 103 cancer, and 73 benign. The second set had 84 cases, 53 cancer, and 31 benign. For detection, the pre-contrast image and the subtraction images of left and right breasts were used as inputs, so the symmetry could be considered. The detected suspicious area was characterized by ResNet50, using three DCE parametric maps as inputs. The results obtained using slice-based analyses were combined to give a lesion-based diagnosis.ResultsIn the first dataset, 101 of 103 cancers were detected by Mask R-CNN as suspicious, and 99 of 101 were correctly classified by ResNet50 as cancer, with a sensitivity of 99/103 = 96%. 48 of 73 benign lesions and 131 normal areas were identified as suspicious. Following classification by ResNet50, only 16 benign and 16 normal areas remained as malignant. The second dataset was used for independent testing. The sensitivity was 43/53 = 81%. Of the total of 121 identified non-cancerous lesions, only 6 of 31 benign lesions and 22 normal tissues were classified as malignant.ConclusionResNet50 could eliminate approximately 80% of false positives detected by Mask R-CNN. Combining Mask R-CNN and ResNet50 has the potential to develop a fully-automatic computer-aided diagnostic system for breast cancer on MRI.

This paper investigates the application of convolutional neural networks (CNNs) for the early detection of brain tumors to enhance diagnostic accuracy. Brain tumors present a significant global health challenge, and early detection is vital for successful treatments and patient outcomes. The study includes a comprehensive literature review of recent advancements in brain tumor detection techniques. The main focus is on the development and evaluation of CNN models, including EfficientNetB3, residual networks-50 (ResNet50) and visual geometry group-19 (VGG-19), for binary image classification using magnetic resonance imaging (MRI) scans. These models demonstrate promising results in terms of accuracy, precision, and recall metrics. However, challenges related to overfitting and limited dataset size are acknowledged. The study highlights the potential of artificial intelligence (AI) in improving brain tumor detection and emphasizes the need for further research and validation in real-world clinical settings. EfficientNetB3 reached 99.44% training accuracy but showed potential overfitting with validation accuracy dropping to 89.47%. ResNet50 steadily improved to 83.62% training accuracy and 89.47% validation accuracy. VGG19 struggled, with only 62% accuracy. [ABSTRACT FROM AUTHOR]

Abstract The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convolutional Neural Network (R-CNN) masks, Grad-CAM and transfer learning, this work offers an effective method for the detection of brain tumours. Helping doctors make extremely accurate diagnoses is the goal. A transfer learning-based model has been suggested that offers high sensitivity and accuracy scores for brain tumour detection when segmentation is done using R-CNN masks. To train the model, the Inception V3, VGG-16, and ResNet-50 architectures were utilised. The Brain MRI Images for Brain Tumour Detection dataset was utilised to develop this method. This work's performance is evaluated and reported in terms of recall, specificity, sensitivity, accuracy, precision, and F1 score. A thorough analysis has been done comparing the proposed model operating with three distinct architectures: VGG-16, Inception V3, and Resnet-50. Comparing the proposed model, which was influenced by the VGG-16, to related works also revealed its performance. Achieving high sensitivity and accuracy percentages was the main goal. Using this approach, an accuracy and sensitivity of around 99% were obtained, which was much greater than current efforts.

Abstract Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such as subjectivity in interpretation and constraints in handling complex image features. This research paper proposes an integrated deep learning approach utilizing pre-trained models—VGG16, ResNet50, and InceptionV3—combined within a unified framework to improve diagnostic accuracy in medical imaging. The method focuses on lung cancer detection using images resized and converted to a uniform format to optimize performance and ensure consistency across datasets. Our proposed model leverages the strengths of each pre-trained network, achieving a high degree of feature extraction and robustness by freezing the early convolutional layers and fine-tuning the deeper layers. Additionally, techniques like SMOTE and Gaussian Blur are applied to address class imbalance, enhancing model training on underrepresented classes. The model’s performance was validated on the IQ-OTH/NCCD lung cancer dataset, which was collected from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over a period of three months in fall 2019. The proposed model achieved an accuracy of 98.18%, with precision and recall rates notably high across all classes. This improvement highlights the potential of integrated deep learning systems in medical diagnostics, providing a more accurate, reliable, and efficient means of disease detection.

Image classification tasks often compress the neural network model to reduce the number of parameters, which leads to a decrease in classification accuracy. herefore, we propose a novel ResNet50-based attention mechanism for image classification. ResNet50 network is used to extract image features and input the features into the graph neural network as node features. Then, packet convolution and depth-separable convolution are used to compress the residual network. The attention mechanism is introduced into the network backbone to make it focus on the important part of the neighborhood and help the branch network to extract key information. The accuracy of 5-way 1-shot task classification on three publicly available datasets reaches 86.32%, 92.21% and 92.19%, respectively. The proposed method has achieved remarkable results in image classification tasks.

Cataract, an age-related eye disease, poses a significant ophthalmological public health challenge in both developed and developing nations. Tailoring treatment or surgery plans helps accurately categorise the cataract's developmental stage. Precise cataract grading helps in diagnosing cataracts and subsequently scheduling surgical intervention. In this project endeavour, a solution is presented to automate the cataract grading process utilizing slit lamp bio-microscope data sets acquired through smartphones. This innovation is particularly valuable for novice practitioners and non-specialist doctors/experts who may struggle with proficiently interpreting cataract progression, leading to potential misdiagnoses. To address this challenge, a Neural Network model is harnessed to automatically predict the grade of cataracts. The study employs multi-class image classification models, including the Convolutional neural network (CNN) model, the Efficient Net B0 model, and the ResNet50 model, for this purpose. Notably, the ResNet50 model outperforms the other models in terms of accuracy and prediction capability for the provided data set. Achieving an accuracy rate of 0.8611, the ResNet50 model demonstrates superior performance in classifying cataract grades, after augmenting the data set with 544 images. This performance comparison establishes the ResNet50 model as the most robust choice among the considered models and data sets.

Breast cancer (BC), the most prevalent cancer worldwide, poses a significant threat to women's health, often resulting in mortality. Early intervention is crucial for reducing mortality rates and improving recovery. Mammography plays a pivotal role in early detection through high-resolution imaging. Various classification techniques, including classical and deep learning (DL) methods, assist in diagnosing BC. Convolutional neural networks (CNN)-based classification with transfer learning enhances efficiency and accuracy, especially with limited datasets. This study evaluates the performance of different pretrained deep CNN architectures in classifying pathological mammography scans from the Mini-MIAS dataset. The results show that Xception, VGG16, VGG19, and MobileNetV2 achieve the highest accuracy (97%), with VGG19 demonstrating the fastest prediction speed (0.53 s). [ABSTRACT FROM AUTHOR]

Skin cancer is a highly lethal disease, and detecting it at an early stage is critical. Skin lesion segmentation is a complex process involving identifying the infected area in an image with low contrast, variable size, and position. This task is essential in medical analysis, as it helps clinicians focus on a specific area of the image before further analysis. Our paper introduces a new method for improving the segmentation of medical images by providing the efficient neural connections to design efficient U-Net architecture. We have utilized skip paths to the encoder and minimize the semantic gap between concatenated feature maps. This leads to more precise segmentation outcomes. We have used the PH2 and ISIC-2018 as benchmark dataset to validate the effectiveness of the proposed approach and surpass the available benhcmark performance. We have obtained approximately 96.18% accuracy with the PH2 dataset and 96.09% accuracy with the ISIC-2018 dataset. The outcomes of our architecture are quite impressive, and they exhibit superior performance over both the baseline model and other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The emergence of audio deepfakes has prompted concerns over reputational integrity and dependability. Deepfakes with audio can now be produced more easily, which makes it harder to spot them. Technologies that can identify audio-level deepfakes must be developed in order to address this issue. As a result, we have recognised the importance of feature extraction for these systems and we have created an improved method for feature extraction. On audio Mel spectrogram, we have employed a modified ResNet50 to extract features. Then, Linear Discriminant Analysis (LDA) dimensionality reduction technique have been used to optimise the feature complexity. The chosen features by LDA are then utilised to train these machine learning (ML) models using the backend classification algorithms Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbour (KNN), and Naive Bayes (NB). The ASVspoof 2019 Logical Access (LA) partition is utilised for training, ASVspoof 2021 deep fake partition are used to evaluate the systems. Also, we have used DECRO dataset for evakuating our proposed model under unseen noisy dataset. We have used 20% audios from training dataset for validation purpose. When compared to other models, our proposed method performs better than traditional feature extraction methods such as Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficients (GTCC). It achieves an impressive Equal Error Rate (EER) of only 0.4% and an accuracy of 99.7%. [ABSTRACT FROM AUTHOR]

For deep model training, an optimization technique is required that minimizes loss and maximizes accuracy. The development of an effective optimization method is one of the most important study areas. The diffGrad optimization method uses gradient changes during optimization phases but does not update 2nd order moments based on 1st order moments, and the AngularGrad optimization method uses the angular value of the gradient, which necessitates additional calculation. Due to these factors, both of those approaches result in zigzag trajectories that take a long time and require additional calculations to attain a global minimum. To overcome those limitations, a novel adaptive deep learning optimization method based on square of first momentum (sqFm) has been proposed. By adjusting 2nd order moments depending on 1st order moments and changing step size according to the present gradient on the non-negative function, the suggested sqFm delivers a smoother trajectory and better image classification accuracy. The empirical research comparing the performance of the proposed sqFm with Adam, diffGrad, and AngularGrad applying non-convex functions demonstrates that the suggested method delivers the best convergence and parameter values. In comparison to SGD, Adam, diffGrad, RAdam, and AngularGrad(tan) using the Rosenbrock function, the proposed sqFm method can attain the global minima gradually with less overshoot. Additionally, it is demonstrated that the proposed sqFm gives consistently good classification accuracy when training CNN networks (ResNet16, ResNet50, VGG34, ResNet18, and DenseNet121) on the CIFAR10, CIFAR100, and MNIST datasets, in contrast to SGDM, diffGrad, Adam, AngularGrad(Cos), and AngularGrad(Tan). The proposed method also gives the best classification accuracy than SGD, Adam, AdaBelief, Yogi, RAdam, and AngularGrad using the ImageNet dataset on the ResNet18 network. Source code link: https://github.com/UtpalNandi/sqFm-A-novel-adaptive-optimization-scheme-for-deep-learning-model. [ABSTRACT FROM AUTHOR]

This paper proposes applying a novel deep‐learning model, TBDLNet, to recognize CT images to classify multidrug‐resistant and drug‐sensitive tuberculosis automatically. The pre‐trained ResNet50 is selected to extract features. Three randomized neural networks are used to alleviate the overfitting problem. The ensemble of three RNNs is applied to boost the robustness via majority voting. The proposed model is evaluated by five‐fold cross‐validation. Five indexes are selected in this paper, which are accuracy, sensitivity, precision, F1‐score, and specificity. The TBDLNet achieves 0.9822 accuracy, 0.9815 specificity, 0.9823 precision, 0.9829 sensitivity, and 0.9826 F1‐score, respectively. The TBDLNet is suitable for classifying multidrug‐resistant tuberculosis and drug‐sensitive tuberculosis. It can detect multidrug‐resistant pulmonary tuberculosis as early as possible, which helps to adjust the treatment plan in time and improve the treatment effect. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a devastating neurologic condition characterized by brain atrophy and neuronal loss, posing a significant global health challenge. Early detection is paramount to impede its progression. This study aims to construct an optimized deep learning (DL) framework for early AD detection and classification using magnetic resonance images (MRI) scans. The classification task involves distinguishing between four AD stages: mild demented (MD), very mild demented (VmD), moderate demented (MoD), and non-demented (ND). To achieve effective classification, three DL models (VGG16, InceptionV3, and ResNet50) are implemented and fine-tuned. A systematic evaluation is conducted to optimize hyper-parameters, with extensive experimentation. The results demonstrate superior classification performance of the customized DL models compared to state-of-the-art methods. Specifically, visual geometry group 16 (VGG16) achieves the highest accuracy of 95.85%, followed by ResNet50 with 89.38%, while InceptionV3 yields the lowest accuracy of 87.23%. This study highlights the critical role of selecting appropriate DL models and customizing them for accurate AD detection and classification across various stages, offering significant insights for advancing clinical diagnosis and treatment strategies. [ABSTRACT FROM AUTHOR]

Classifying vehicles in real time was necessary to manage and plan road traffic and avoid frequent traffic jams, traffic violations, and fatal traffic accidents. However, detecting vehicles at night presents a significant challenge, requiring the classification algorithm to be tested under diverse conditions, such as rainy weather, cloudy weather, low illumination, and others, which makes identifying vehicles a complicated task. This paper detected and classifiess vehicle through YOLO-v2, ResNet50, and an optimally configured Long Short-Term Memory (LSTM). But figuring out the best hyperparameters by trial and error took longer and was more complicated. The research resolved the computational time and complexity by involving Oppositional-based Wild Horse Optimization (OWHO) techniques to identify the optimal hyperparameters for LSTM. The result showed that the proposed technique was better, with an average accuracy of 97.38% in classifying vehicles, which was better than other techniques. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Brain tumors are abnormal cell growths in the brain, posing significant treatment challenges. Accurate early detection using non-invasive methods is crucial for effective treatment. This research focuses on improving the early detection of brain tumors in MRI images through advanced deep-learning techniques. The primary goal is to identify the most effective deep-learning model for classifying brain tumors from MRI data, enhancing diagnostic accuracy and reliability. Methods: The proposed method for brain tumor classification integrates segmentation using K-means++, feature extraction from the Spatial Gray Level Dependence Matrix (SGLDM), and classification with ResNet50, along with synthetic data augmentation to enhance model robustness. Segmentation isolates tumor regions, while SGLDM captures critical texture information. The ResNet50 model then classifies the tumors accurately. To further improve the interpretability of the classification results, Grad-CAM is employed, providing visual explanations by highlighting influential regions in the MRI images. Result: In terms of accuracy, sensitivity, and specificity, the evaluation on the Br35H::BrainTumorDetection2020 dataset showed superior performance of the suggested method compared to existing state-of-the-art approaches. This indicates its effectiveness in achieving higher precision in identifying and classifying brain tumors from MRI data, showcasing advancements in diagnostic reliability and efficacy. Discussion: The superior performance of the suggested method indicates its robustness in accurately classifying brain tumors from MRI images, achieving higher accuracy, sensitivity, and specificity compared to existing methods. The method's enhanced sensitivity ensures a greater detection rate of true positive cases, while its improved specificity reduces false positives, thereby optimizing clinical decision-making and patient care in neuro-oncology. [ABSTRACT FROM AUTHOR]

Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such as subjectivity in interpretation and constraints in handling complex image features. This research paper proposes an integrated deep learning approach utilizing pre-trained models—VGG16, ResNet50, and InceptionV3—combined within a unified framework to improve diagnostic accuracy in medical imaging. The method focuses on lung cancer detection using images resized and converted to a uniform format to optimize performance and ensure consistency across datasets. Our proposed model leverages the strengths of each pre-trained network, achieving a high degree of feature extraction and robustness by freezing the early convolutional layers and fine-tuning the deeper layers. Additionally, techniques like SMOTE and Gaussian Blur are applied to address class imbalance, enhancing model training on underrepresented classes. The model's performance was validated on the IQ-OTH/NCCD lung cancer dataset, which was collected from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over a period of three months in fall 2019. The proposed model achieved an accuracy of 98.18%, with precision and recall rates notably high across all classes. This improvement highlights the potential of integrated deep learning systems in medical diagnostics, providing a more accurate, reliable, and efficient means of disease detection. [ABSTRACT FROM AUTHOR]

In view of the scarcity of artificial labels and poor classification performance in CT(Computed Tomography) image analysis, a self-supervised CT image classification method combining in-slice semantic and interslice structural features is proposed in this study. In this method, the hierarchical structure of CT images and the semantic features of local components are utilized to process the unlabeled lesion images through the confusion section generation algorithm, and the spatial index and confusion section are generated as supervisory information. In the self-supervised auxiliary task, the ResNet50 network was used to extract both the intraslice semantic and interslice structural features related to the lesion site from the confused sections, and the learned features were transferred to the subsequent medical classification task, so that the final model gained from the unlabeled data. The experimental results show that compared with other 2D and 3D models for CT images, the proposed method can achieve better classification performance and label utilization efficiency when the used labeled data is limited. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a significant healthcare concern, and the identification of high-risk patients is crucial. Indeed, gastric precancerous conditions present significant diagnostic challenges, particularly early intestinal metaplasia (IM) detection. This study developed a deep learning system to assist in IM detection using image patches from gastric corpus examined using virtual chromoendoscopy in a Western country. Utilizing a retrospective dataset of endoscopic images from Sant'Andrea University Hospital of Rome, collected between January 2020 and December 2023, the system extracted 200 × 200 pixel patches, classifying them with a voting scheme. The specificity and sensitivity on the patch test set were 76% and 72%, respectively. The optimization of a learnable voting scheme on a validation set achieved a specificity of 70% and sensitivity of 100% for entire images. Despite data limitations and the absence of pre-trained models, the system shows promising results for preliminary screening in gastric precancerous condition diagnostics, providing an explainable and robust Artificial Intelligence approach. [ABSTRACT FROM AUTHOR]

In this research, we introduce a network that can identify pneumonia, COVID-19, and tuberculosis using X-ray images of patients' chests. The study emphasizes tuberculosis, COVID-19, and healthy lung conditions, discussing how advanced neural networks, like VGG16 and ResNet50, can improve the detection of lung issues from images. To prepare the images for the model's input requirements, we enhanced them through data augmentation techniques for training purposes. We evaluated the model's performance by analyzing the precision, recall, and F1 scores across training, validation, and testing datasets. The results show that the ResNet50 model outperformed VGG16 with accuracy and resilience. It displayed superior ROC AUC values in both validation and test scenarios. Particularly impressive were ResNet50's precision and recall rates, nearing 0.99 for all conditions in the test set. On the hand, VGG16 also performed well during testing—detecting tuberculosis with a precision of 0.99 and a recall of 0.93. Our study highlights the performance of our deep learning method by showcasing the effectiveness of ResNet50 over traditional approaches like VGG16. This progress utilizes methods to enhance classification accuracy by augmenting data and balancing them. This positions our approach as an advancement in using state-of-the-art deep learning applications in imaging. By enhancing the accuracy and reliability of diagnosing ailments such as COVID-19 and tuberculosis, our models have the potential to transform care and treatment strategies, highlighting their role in clinical diagnostics. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to generate defect images of body parts using a GAN (generative adversarial network) and compare and analyze the performance of the YOLO (You Only Look Once) v7 and v8 object detection models. The goal is to accurately judge good and defective products. Quality control is very important in the automobile industry, and defects in body parts directly affect vehicle safety, so the development of highly accurate defect detection technology is essential. This study ensures data diversity by generating defect images of car body parts using a GAN and through this, compares and analyzes the object detection performance of the YOLO v7 and v8 models to present an optimal solution for detecting defects in car parts. Through experiments, the dataset was expanded by adding fake defect images generated by the GAN. The performance experiments of the YOLO v7 and v8 models based on the data obtained through this approach demonstrated that YOLO v8 effectively identifies objects even with a smaller amount of data. It was confirmed that defects could be detected. The readout of the detection system can be improved through software calibration. [ABSTRACT FROM AUTHOR]

The segmentation of cancerous tumours, particularly brain tumours, is of paramount importance in medicine due to its crucial role in accurately determining the extent of tumour lesions. However, conventional segmentation approaches have proven less effective in accurately delineating the exact extent of brain tumours, in addition to representing a time-consuming task, making it a laborious process for clinicians. In this study, we proposed an automatic segmentation method based on convolutional neural networks (CNNs), by developing a new model using the Resnet50 architecture for detection and the DrvU-Net architecture, derived from the U-Net model, with adjustments adapted to the characteristics of the medical imaging data for the segmentation of a publicly available brain image dataset called TCGA-LGG and TCIA. Following an in-depth comparison with other recent studies, our model has demonstrated its effectiveness in the detection and segmentation of brain tumours, with accuracy rates for accuracy and the Dice Similarity Coefficient (DSC), the Similarity Index (IoU) and the Tversky Coefficient reaching 96%, 94%, 89% and 91.5% respectively. [ABSTRACT FROM AUTHOR]

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. [ABSTRACT FROM AUTHOR]

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Acute lymphoblastic leukemia is a very important cancer in childhood but quite prominent in later years of life for the genetic defects in lymphoid progenitors, which are hallmarks of the disease. In children, ALL mostly affects those aged between 2 and 6 years old and, against the background of contemporary biology knowledge and treatment approaches, is associated with more than 80% cure rates. However, approximately 20% of children with ALL relapse; therefore, there is a huge need for better risk identification and treatment optimization. In adults, ALL mainly affects B-cell precursors and is treated with chemotherapy and, in some cases, stem cell transplantation. An accurate and early diagnosis of ALL is of key importance but difficult to realize due to morphological similarities between normal cells and leukemic cells. This study, therefore, proposes a CNN model to improve diagnostic accuracy. Furthermore, it exploits the capabilities of CNN in feature extraction with Adamax Optimizer and the Categorical Cross-Entropy Loss Function to deal with imbalances and noise in the dataset. RESNET50-CNN has achieved 98.63% accuracy in classification and is hence a very strong tool in ALL detection and classification. [ABSTRACT FROM AUTHOR]

Growing mangoes is an important part of life in southern India and a major economic driver for the area. Nevertheless, several leaf diseases often impede mango tree development and production, substantially affecting harvest output and quality. Detecting and identifying mango leaf diseases early can be challenging due to the diverse crop varieties, climatic circumstances, and numerous disease signs. While deep-learning methods have been developed to address this problem, they generally need help to detect illnesses across geographies and crop types. To tackle this difficulty, this research offers a transfer learning model that uses Explainable Artificial Intelligence (XAI) characteristics to identify and categorize leaf diseases. This research proposes MLTNet (Mango Leaf Disease Classification with Transfer Learning, Feature Localization, and Visual Explanations) in this study. Our study utilized a dataset from Southern India comprising 1,275 high-quality images of mango leaves affected by diseases like rust and powdery mildew, augmented to 11,480 images across 14 classes to enhance model training and robustness. This novel model utilizes Explainable Artificial Intelligence (XAI) techniques such that leaf disease detection and categorization may achieve higher levels of accuracy. The research work lies in the development of the MLTNet model, which integrates Explainable Artificial Intelligence (XAI) techniques with the ResNet50 architecture to enhance classification accuracy in mango leaf disease detection. This model uniquely employs advanced data pre-processing methods like Error Level Analysis and incorporates Grad-CAM for feature localization and visual explanations. We compared MLTNet's performance with state-of-the-art models like ResNet-50, VGG-16, and InceptionV3, focusing on accuracy, interpretability, and computational efficiency. MLTNet demonstrated superior performance, achieving a training accuracy of 94.3% and a test accuracy of 86.3%, which notably surpasses other models under similar conditions. This success is attributed to the model's ability to leverage complex features from the augmented dataset and the added interpretability provided by XAI techniques. [ABSTRACT FROM AUTHOR]

The essentiality of categorizing dry foods plays a crucial role in maintaining quality control and ensuring food safety for human consumption. The effectiveness and precision of classification methods are vital for enhanced evaluation of food quality and streamlined logistics. To achieve this, we gathered a dataset of 11,500 samples from Mendeley and proceeded to employ various transfer learning models, including VGG16 and ResNet50. Additionally, we introduce a novel hybrid model, VGG16-ResNet, which combines the strengths of both architectures. Transfer learning involves utilizing knowledge acquired from one task or domain to enhance learning and performance in another. By fusing multiple Deep Learning techniques and transfer learning strategies, such as VGG16-ResNet50, we developed a robust model capable of accurately classifying a wide array of dry foods. The integration of Deep Learning (DL) and transfer learning techniques in the context of dry food classification signifies a drive towards automation and increased efficiency within the food industry. Notably, our approach achieved remarkable results, achieving a classification accuracy of 99.78% for various dry food images, even when dealing with limited training data for VGG16-ResNet50. [ABSTRACT FROM AUTHOR]

The structural health monitoring data of the Research Institute of Highway Ministry of Transportation Track (RIOHTRACK) are huge and complex, including a large amount of dynamic high-frequency sensor data of road underground structures. However, detecting anomalies in the overall distribution of the whole loading cycle data is difficult for traditional numerical data analysis methods. This study proposes an anomaly detection method that visualizes numerical values and designs a deep convolutional neural network DCNN6 for image classification to achieve anomaly detection of large-scale dynamic high-frequency sensor data. After training, the detection rate of DCNN6 for abnormal data reached 92.3% for the validation set. Compared with Residual Neural Network (ResNet50) and GhostNet, the detection accuracy of the method proposed in this study increased by 69% and 4%, respectively, reaching 97%, and the detection speeds were also faster by 5 s/epoch and 4 s/epoch, respectively. Therefore, the proposed method can accurately and quickly detect the abnormality of the dynamic high-frequency sensor data of underground structures, which can provide data support for quickly discovering that the vehicle deviates from the preset trajectory, rectifying the driver's driving deviation, analyzing the force of the whole road area, and grasping the evolution law of the rut. [ABSTRACT FROM AUTHOR]

Facial retouching, also referred to as digital retouching, is the process of modifying or enhancing facial characteristics in digital images or photographs. While it can be a valuable technique for fixing flaws or achieving a desired visual appeal, it also gives rise to ethical considerations. This study involves categorizing genuine and retouched facial images from the standard ND-IIITD retouched faces dataset using a transfer learning methodology. The impact of different primary optimization algorithms--specifically Adam, RMSprop, and Adadelta--utilized in conjunction with a fine-tuned ResNet50 model is examined to assess potential enhancements in classification effectiveness. Our proposed transfer learning ResNet50 model demonstrates superior performance compared to other existing approaches, particularly when the RMSprop and Adam optimizers are employed in the fine-tuning process. By training the transfer learning ResNet50 model on the ND-IIITD retouched faces dataset with the "ImageNet" weight, we achieve a validation accuracy of 98.76%, a training accuracy of 98.32%, and an overall accuracy of 98.52% for classifying real and retouched faces in just 20 epochs. Comparative analysis indicates that the choice of optimizer during the fine-tuning of the transfer learning ResNet50 model can further enhance the classification accuracy. [ABSTRACT FROM AUTHOR]

Mango fruits are highly valued for their taste, flavor, and nutritional value, making them a popular choice among consumers. However, mango fruits are susceptible to various diseases that can significantly affect their yield and quality. Therefore, accurate and timely detection of these diseases is crucial for effective disease management and minimizing losses in mango production. Computer-aided diagnosis techniques have emerged as a promising tool for disease detection and classification in mango fruits. This study adopts an image classification approach to identify various diseases in mangos and distinguish them from healthy specimens. The pre-processing phase involves a Wiener filter for noise removal, followed by Otsu's threshold-based segmentation as a crucial operation. Subsequently, features are extracted by implementing the ResNet50 model. The proposed model was experimentally verified and validated, demonstrating optimal results with an accuracy of 98.25%. This high accuracy rate highlights the effectiveness of the XG-Boost classifier in accurately categorizing mango images into different disease categories. The experimental results strongly support the potential practical application of the model in the agricultural industry for disease detection in mango crops. [ABSTRACT FROM AUTHOR]

Skin cancer diagnosis, particularly melanoma detection, is an important healthcare concern worldwide. This study uses the ISIC2017 dataset to evaluate the performance of three deep learning architectures, VGG16, ResNet50, and InceptionV3, for binary classification of skin lesions as benign or malignant. ResNet50 achieved the highest training-set accuracy of 81.1%, but InceptionV3 outperformed the other classifiers in generalization with a validation accuracy of 76.2%. The findings reveal the various strengths and trade-offs of alternative designs, providing important insights for the development of dermatological decision support systems. This study contributes to the progress of automated skin cancer diagnosis and establishes the framework for future studies aimed at improving classification accuracy. [ABSTRACT FROM AUTHOR]

Leukemia is a form of blood cancer that results in an increase in the number of white blood cells in the body. The correct identification of leukemia at any stage is essential. The current traditional approaches rely mainly on field experts' knowledge, which is time consuming. A lengthy testing interval combined with inadequate comprehension could harm a person's health. In this situation, an automated leukemia identification delivers more reliable and accurate diagnostic information. To effectively diagnose acute lymphoblastic leukemia from blood smear pictures, a new strategy based on traditional image analysis techniques with machine learning techniques and a composite learning approach were constructed in this experiment. The diagnostic process is separated into two parts: detection and identification. The traditional image analysis approach was utilized to identify leukemia cells from smear images. Finally, four widely recognized machine learning algorithms were used to identify the specific type of acute leukemia. It was discovered that Support Vector Machine (SVM) provides the highest accuracy in this scenario. To boost the performance, a deep learning model Resnet50 was hybridized with this model. Finally, it was revealed that this composite approach achieved 99.9% accuracy. [ABSTRACT FROM AUTHOR]