Web-based Explainable Deep Learning Framework for Skin Disease Detection

We have developed a controlled comparative framework
for evaluating six CNN and Transformer architectures on
the HAM10000 dataset under a consistent experimental
protocol.
• We have employed a two-phase transfer learning strategy
consisting of initial feature extraction with frozen backbones
followed by selective fine-tuning using a reduced learning
rate.
• We have incorporated random oversampling to address the
substantial class imbalance in the HAM10000 dataset and
improve the representation of minority disease categories
during training.
• We have integrated GradCAM++ into the classification
pipeline to provide class-specific visual explanations of
model predictions.
• We have evaluated the models using accuracy, macroprecision, macro-recall, macro-F1 score, macro-AUC, and
inference time to provide a comprehensive performance
comparison.
• We have integrated the classification and explainability
components into a responsive web-based application to
demonstrate an accessible prototype for dermoscopic image
analysis.