Development of a Convolutional Neural Network-Based System for Skin Disease Classification

This research presents the development, implementation, and comprehensive evaluation of an advanced deep learning system based on convolutional neural networks (CNNs) for the automated classification of skin diseases. Skin disorders constitute a significant global health burden, affecting approximately 25The system was trained and validated on a diverse and demographically representative dataset comprising [X] dermatological images (including both clinical photographs and dermoscopic images) spanning [Y] distinct skin conditions. Particular attention was paid to ensure inclusion of images representing various skin tones, age groups, and anatomical sites to improve generalizability. The model under- went rigorous evaluation using a multi-metric framework and achieved an overall accuracy of [Z]Visualization techniques including Gradient-weighted Class Activation Mapping (Grad-CAM) revealed that the model focused on clinically rele- vant morphological features, suggesting its potential interpretability in clinical settings. Ablation studies confirmed the significant contributions of our architectural modifications and training strategies to overall performance. While perfor- mance variations across different demographic groups were observed, with slightly lower accuracy for darker skin tones, these findings highlight areas for future improvement. The promising results suggest that CNN-based approaches hold considerable potential as decision support tools for dermatological diagnosis, potentially improving early detection rates and healthcare outcomes in dermatology, particularly in resource-constrained environments. Limitations including con- text dependency and the need for prospective clinical validation are acknowledged and discussed as directions for future research.