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Skin cancer is one of the most common malignancies worldwide, where early detection significantly improves treatment outcomes. While deep learning models show promise for automated skin lesion classification, their lack of interpretability limits clinical adoption. This study presents a comprehensive comparative analysis of three convolutional neural networks, ResNet-50, GoogLeNet, and SqueezeNet, for binary skin lesion classification (benign vs. malignant), integrating three explainable AI (XAI) methods (Grad-CAM, LIME, and Occlusion Sensitivity) to enhance clinical interpretability. We trained and evaluated these architectures on the Kaggle Skin Cancer dataset, which contains 2,637 dermoscopic images (1,440 benign, 1,197 malignant). Transfer learning employed ImageNet pre-trained weights with two-stage fine-tuning. Performance was assessed using accuracy, precision, recall, F1-score, specificity, and AUC-ROC metrics. ResNet-50 achieved the highest accuracy of 91.36% with an excellent AUC of 0.9721, demonstrating superior balanced performance. GoogLeNet achieved 88.94% accuracy with 73% fewer parameters, offering an optimal accuracy-efficiency trade-off. The proposed lightweight CNN, despite having the fewest parameters (1.2M), achieved 85.45% accuracy and a malignancy detection sensitivity of 92.7%, making it well-suited for screening applications. Training times ranged from 1.5 minutes (SqueezeNet) to 3 minutes 39 seconds (ResNet-50), demonstrating feasibility for resource-constrained settings. All XAI methods successfully generated clinically meaningful explanations, with models consistently focusing on lesion centers, color variations, and irregular borders. This study demonstrates that combining deep learning with XAI enables accurate and interpretable skin cancer detection. ResNet-50 is well-suited to well-resourced clinical settings, GoogLeNet offers balanced performance for resource-constrained deployments, and SqueezeNet enables mobile telemedicine applications with superior sensitivity. ABSTRAK: Kanser kulit merupakan antara malignansi yang paling lazim di seluruh dunia, dan pengesanan awal terbukti dapat meningkatkan keberkesanan rawatan secara signifikan. Walaupun model pembelajaran mendalam menunjukkan potensi tinggi dalam pengelasan automatik lesi kulit, kekurangan kebolehinterpretasian telah mengehadkan penerimaan klinikal. Kajian ini membentangkan analisis perbandingan menyeluruh terhadap tiga rangkaian neural konvolusi, iaitu ResNet-50, GoogLeNet, dan SqueezeNet, bagi pengelasan binari lesi kulit (jinak vs. malignan), digabungkan dengan tiga kaedah kecerdasan buatan boleh jelas (XAI), iaitu Grad-CAM, LIME, dan Kepekaan Halangan, bagi menyokong interpretasi klinikal. Model dilatih dan dinilai menggunakan set data Kanser Kulit Kaggle yang mengandungi 2,637 imej dermoskopi, dengan menggunakan pembelajaran pindahan berasaskan pemberat pralatih ImageNet dan penalaan halus dua peringkat. Penilaian prestasi menggunakan metrik ketepatan, ketepatan ramalan, kepekaan, skor F1, pengkhususan, dan AUC-ROC menunjukkan bahawa ResNet-50 mencapai prestasi tertinggi dengan ketepatan 91.36% dan AUC 0.9721, manakala GoogLeNet menawarkan keseimbangan optimum antara ketepatan dan kecekapan dengan pengurangan parameter sebanyak 73%. SqueezeNet, walaupun paling ringan, mencapai kepekaan pengesanan malignan tertinggi sebanyak 92.7%, menjadikannya sesuai untuk aplikasi saringan dan teleperubatan mudah alih. Semua kaedah XAI berjaya menghasilkan penjelasan bermakna secara klinikal, dengan fokus konsisten pada pusat lesi, variasi warna, dan sempadan tidak sekata. Secara keseluruhan, kajian ini membuktikan bahawa penggabungan pembelajaran mendalam dan XAI membolehkan pengesanan kanser kulit yang tepat, boleh ditafsir, dan sesuai dalam pelbagai kekangan sumber klinikal.

Traditional wound healing methods struggled with active tissues enduring ongoing motion and biocompatibility. The core objective of this study is to engineer a series of PVA-based wound-healing films containing different additives like acetic acid, Magnesium and Alpha-ketoglutarate. In-vitro tests like swelling, degradation, contact angle, pH sensitivity and moisture content were performed to assess the biodegradability, biocompatibility, hydrophilicity and hygroscopicity of the samples. For in-vivo use, film reactions in the body and excretion time are crucial, whereas hydrophilicity and hygroscopicity would determine the adhesion of the films to the wound and potential inflammatory reactions it may cause. Among the four experimental samples (PVA, PVA/AcOH, PVA/Mg/AcOH and PVA/AcOH/Mg/AKG) it was found that PVA/AcOH exhibited the highest degree of hydrophilicity with the contact angle of 64.5% ± 2% whereas all samples appeared stable in acidic, neutral and basic conditions ruling out the potential for inflammation and/or disintegration in in-vivo environment. PVA/AcOH and PVA/AcOH/Mg showed a stable swelling rate which would maintain the mechanical stability and function inside the body, while PVA/AcOH and PVA/AcOH/Mg/AKG linearly degraded. Lastly, PVA/Mg/AcOH/AKG displayed high hygroscopicity, next to PVA/Mg/AcOH which was second second-highest. Statistically analysis concluded PVA/Mg/AcOH as the best-suited material for fabrication of wound healing suture films.

Alzheimer’s disease is one of the most common neuropathological diseases worldwide, with approximately 46.8 million individuals suffering from Alzheimer’s disease with ramifications for caregivers and economies. Various studies describe Alzheimer’s as a progressive disease and focus on the need for early detection to allow for timely intervention and treatment options. Cognitive tests are typically used to evaluate early detection for Alzheimer’s but MRI brain scans are the main detection method for Alzheimer’s diagnosis. Several studies have looked for abnormal brain conditions based on Alzheimer’s disease and dementia disease detection from features extracted from medical images. Methodological approaches to deep learning for brain structure segmentation and Alzheimer’s disease classification are going to become more common due to their better performance on larger datasets and outperforming established machine learning comprehension models. The use of deep learning techniques in this study is based on ‘‘brain MRI scan classification framework’’ for a more precise, efficient, and automated classification of Alzheimer’s Disease stage. The suggested deep learning model uses a convolutional neural network (CNN) architecture with depth-wise separable convolutions based on MobileNetV1 to allow for computational efficiency, pattern recognition and robust feature extraction assistance. The study employed 6400 MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, categorized as non-demented, mildly demented, moderately demented, and severely demented. The model achieved an overall accuracy of 98% on the test dataset, demonstrating an ability to identify the discrete classes of Alzheimer's disease progression. The average test loss score was remarkably low at 0.0543, demonstrating an effective reduction of the differences between the predicted value and actual value. The model obtained precision scores of 0.95, 0.97, 1.0, and 1.0. The precision score indicates accuracy in the predictions from the classes. The macro and weighted average precision scores were 0.98, which indicates consistency in the precision across all classes.

The peripheral nerves of the human hand play a crucial role in sensorimotor function. However, their complex anatomy, multiscale biomechanics, and electrically coupled behavior make them difficult to model computationally. Recent advances in medical imaging, finite element modeling, and data-driven methods have enabled the development of increasingly realistic models of nerve mechanics and signal conduction. The results obtained from modeling nerve mechanics and electrical conduction can provide useful insight into the underlying mechanisms of several neuropathies such as carpal tunnel syndrome (CTS), diabetic neuropathy, and traumatic nerve injury. This systematic review examined key modeling strategies, including the use of anatomical reconstruction, finite element analysis (FEA) for stress and strain analysis, computer simulations of the electrophysiological activity of nerves to model nerve conduction, multi-physics analyses, and artificial intelligence (AI)-assisted modeling approaches. A systematic literature search was conducted in PubMed, IEEE Xplore, Scopus, and Web of Science, covering studies published up to December 2025 to identify studies related to computational and AI-assisted modeling of the human hand's peripheral nerves. After removing duplicates, screening, and eligibility assessment, 50 studies were included. The literature was synthesized across four thematic areas: anatomical reconstruction and imaging-based models, biomechanical and finite element modeling, electrophysiological and conduction models, and AI-assisted data-driven approaches. Comparative analysis reveals substantial variability in the modeling assumptions, validation strategies, and clinical relevance, with limited consensus on standardized evaluation metrics. Persistent gaps include the inadequate representation of nerve branching networks, insufficient patient-specific connective tissue modeling, and weak experimental or clinical validation. Despite advances in peripheral nerve modeling, no comprehensive review exists focusing specifically on anatomically accurate computational modeling of the human hand's nerves. Future work should improve the alignment of imaging and computational pipelines, develop hybrid electro-mechanical and AI-enhanced frameworks, and enhance translation into surgical planning and neuroprosthetics.