Skin tumors, especially melanoma, which is highly aggressive and progresses quickly to other sites, are an issue in various parts of the world. Nevertheless, the one and only way to save lives is to detect it at its initial stages. This study explores the application of advanced deep learning models for classifying benign and malignant melanoma using dermoscopic images. The aim of the study is to enhance the accuracy and efficiency of melanoma diagnosis with the ConvNeXt, Vision Transformer (ViT) Base-16, and Swin Transformer V2 Small (Swin V2 S) deep learning models. The ConvNeXt model, which integrates principles of both convolutional neural networks and transformers, demonstrated superior performance, with balanced precision and recall metrics. The dataset, sourced from Kaggle, comprises 13,900 uniformly sized images, preprocessed to standardize the inputs for the models. Experimental results revealed that ConvNeXt achieved the highest diagnostic accuracy among the tested models. Experimental results revealed that ConvNeXt achieved an accuracy of 91.5%, with balanced precision and recall rates of 90.45% and 92.8% for benign cases, and 92.61% and 90.2% for malignant cases, respectively. The F1-scores for ConvNeXt were 91.61% for benign cases and 91.39% for malignant cases. This research points out the potential of hybrid deep learning architectures in medical image analysis, particularly for early melanoma detection.

With the growing complexity of skin disorders and the challenges of traditional diagnostic methods, AI offers exciting new solutions that can enhance the accuracy and efficiency of dermatological assessments. Reflectance Confocal Microscopy (RCM) stands out as a non-invasive imaging technique that delivers detailed views of the skin at the cellular level, proving its immense value in dermatology. The manual analysis of RCM images, however, tends to be slow and inconsistent. By combining artificial intelligence (AI) with RCM, this approach introduces a transformative shift toward precise, data-driven dermatopathology, supporting more accurate patient stratification, tailored treatments, and enhanced dermatological care. Advancements in AI are set to revolutionize this process. This paper explores how AI, particularly Convolutional Neural Networks (CNNs), can enhance RCM image analysis, emphasizing machine learning (ML) and deep learning (DL) methods that improve diagnostic accuracy and efficiency. The discussion highlights AI’s role in identifying and classifying skin conditions, offering benefits such as a greater consistency and a reduced strain on healthcare professionals. Furthermore, the paper explores AI integration into dermatological practices, addressing current challenges and future possibilities. The synergy between AI and RCM holds the potential to significantly advance skin disease diagnosis, ultimately leading to better therapeutic personalization and comprehensive dermatological care.

(1) Background: The purpose of this review is to explore how advanced sensor technologies and AI-driven methods, like machine learning and image processing, are shaping non-destructive imaging (NDI) systems. NDI plays a vital role in ensuring the strength and reliability of composite materials. Recent advancements in sensor technologies and AI-driven methods, such as machine learning and image processing, have opened up new ways to improve NDI systems, offering exciting opportunities for better performance. (2) Methods: This review takes a close look at how advanced sensor technologies and machine learning techniques are being integrated into NDI systems. The review evaluates how effective these technologies are at detecting defects and examines their strengths, limitations, and challenges. (3) Results: Combining sensor technologies with AI methods has shown a clear boost in defect detection accuracy and efficiency. However, challenges like high computational requirements and integration costs remain. Despite these hurdles, the potential for these technologies to revolutionize NDI systems is significant. (4) Conclusions: By synthesizing the latest research, this review offers a comprehensive understanding of how sensor technologies are enhancing NDI. The findings highlight their importance for improving defect detection and their broader impact on research and industry, while also pointing out areas where further development is needed for future growth.

The present study seeks to improve the accuracy and reliability of disease identification in apple fruits and leaves through the use of state-of-the-art deep learning techniques. The research investigates several state-of-the-art architectures, such as Xception, InceptionV3, InceptionResNetV2, EfficientNetV2M, MobileNetV3Large, ResNet152V2, DenseNet201, and NASNetLarge. Among the models evaluated, ResNet152V2 performed best in the classification of apple fruit diseases, with a rate of 92%, whereas Xception proved most effective in the classification of apple leaf diseases, with 99% accuracy. The models were able to correctly recognize familiar apple diseases like blotch, scab, rot, and other leaf infections, showing their applicability in agriculture diagnosis. An important by-product of this research is the creation of a web application, easily accessible using Gradio, to conduct real-time disease detection through the upload of apple fruit and leaf images by users. The app gives predicted disease labels along with confidence values and elaborate information on symptoms and management. The system also includes a visualization tool for the inner workings of the neural network, thereby enabling higher transparency and trust in the diagnostic process. Future research will aim to widen the scope of the system to other crop species, with larger disease databases, and to improve explainability further to facilitate real-world agricultural application.

Background/Objectives: Insufficient blood supply to the brain, whether due to blocked arteries (ischemic stroke) or bleeding (hemorrhagic stroke), leads to brain cell death and cognitive impairment. Ischemic strokes, which are more common, occur when blood flow to the brain is obstructed. Magnetic resonance imaging (MRI) scans are essential for distinguishing stroke types, but precise and timely identification of ischemic strokes is crucial for effective treatment. Manual diagnosis can be difficult due to high patient volumes and time constraints in hospitals. This study aims to investigate the use of deep learning techniques for predicting ischemic strokes with high accuracy, enabling earlier diagnosis and intervention. Methods: The study utilized advanced deep learning algorithms, specifically ConvNeXt Base, to analyze large datasets of medical imaging data, focusing on MRI scans. The model was trained and validated on a labeled dataset to identify critical indicators and patterns associated with stroke risk. The performance of the model was evaluated based on accuracy metrics to determine its predictive capabilities. Results: ConvNeXt Base achieved an overall accuracy of 84% on the validation set, demonstrating its effectiveness in identifying ischemic strokes. The model was able to detect key patterns linked to stroke risk, highlighting its potential for use in clinical settings to aid in early diagnosis and decision-making. Conclusions: ConvNeXt Base reveals promise in improving stroke prediction accuracy, enabling earlier diagnosis and personalized treatment, which could lead to faster, more effective medical interventions.

Stroke is a major global cause of death and disability and necessitates both quick diagnosis and treatment within narrow windows of opportunity. CT scanning is still the first-line imaging in the acute phase, but correct interpretation may not always be readily available and may not be resource-available in poor and rural health systems. Automated stroke classification systems can offer useful diagnostic assistance, but clinical application demands high accuracy and explainable decision-making to maintain physician trust and patient safety. In this paper, a ResNet-18 model was trained on 6653 CT brain scans (hemorrhagic stroke, ischemia, normal) with two-phase fine-tuning and transfer learning, XRAI explainability analysis, and web-based clinical decision support system integration. The model performed with 95% test accuracy with good performance across all classes. This system has great potential for emergency rooms and resource-poor environments, offering quick stroke evaluation when specialists are not available, particularly by rapidly excluding hemorrhagic stroke and assisting in the identification of ischemic stroke, which are critical steps in considering tissue plasminogen activator (tPA) administration within therapeutic windows in eligible patients. The combination of classification, explainability, and clinical interface offers a complete framework for medical AI implementation.