A Comparative Evaluation of CNN and Transfer Learning Models for Multi-Class Skin Disease Classification

The automated classification of skin diseases has the potential to significantly enhance patient outcomes through early and targeted treatment. In this study, we compared a custom convolutional neural network (CNN) with three transfer learning architectures—ResNet50, DenseNet201, and Inception—for classifying nine different dermatological conditions. The models were trained on the publicly available FYP Skin Disease Dataset, which consists of 4500 images of both prevalent and serious skin diseases including melanoma, basal cell carcinoma, and squamous cell carcinoma. We conducted extensive performance analysis using accuracy, loss curves, confusion matrices, and multi-class ROC analysis. Our findings indicate that the custom CNN achieved 100% validation accuracy, followed by DenseNet201 at 99% and Inception at 98%, with ResNet50 lagging at 44%. These results highlight the potential of both custom architectures and transfer learning models in dermatological image analysis, which have significant implications for researchers and clinicians seeking viable automated solutions for skin disease diagnosis.