A Transfer Learning System for Skin Disease Classification Using EfficientNet-B5 with Grad-CAM Explainability

Accurate medical diagnostics for skin affections such as skin cancer, psoriasis, vascular tumors, or exanthems have become increasingly difficult due to the growing volume and visual variability of dermatological cases, as well as limited specialist availability. To address this, the present work introduces a complete and deployable deep-learning-based system capable of detecting ten distinct skin disease categories, trained using transfer learning with EfficientNet-B5 and enhanced with explainable AI through Grad-CAM. The proposed system achieves a top-3 accuracy of 95.96%, a weighted F1-score of 0.87, and class-specific F1-scores reaching 0.96 for acne and 0.95 for nail fungus. These results demonstrate strong predictive performance for the deep learning model trained, validated, and evaluated on a ten-class subset of the Dermnet dataset. The research conducted covers the visual explainability of the AI model classification process, including integration into a fully functional web application, usable as an expert system for image uploading, data processing and visualization of results. The AI visualizing technology based on Grad-CAM provides clear, class-specific heatmaps that highlight the most influential regions in each prediction, improving transparency and supporting clinical interpretability.