XAI-Driven Skin Disease Classification: Leveraging GANs to Augment ResNet-50 Performance

TL;DR

This paper presents an AI system that combines GAN-based data augmentation, a fine-tuned ResNet-50 classifier, and explainability techniques to improve skin disease diagnosis accuracy and transparency, addressing data imbalance and black-box issues.

Contribution

It introduces a novel framework integrating DCGANs, ResNet-50, and XAI methods for accurate, interpretable skin disease classification with high performance metrics.

Findings

Achieved 92.50% accuracy and 98.82% Macro-AUC.

Outperformed previous benchmark architectures.

Validated the framework's clinical interpretability.

Abstract

Accurate and timely diagnosis of multi-class skin lesions is hampered by subjective methods, inherent data imbalance in datasets like HAM10000, and the "black box" nature of Deep Learning (DL) models. This study proposes a trustworthy and highly accurate Computer-Aided Diagnosis (CAD) system to overcome these limitations. The approach utilizes Deep Convolutional Generative Adversarial Networks (DCGANs) for per class data augmentation to resolve the critical class imbalance problem. A fine-tuned ResNet-50 classifier is then trained on the augmented dataset to classify seven skin disease categories. Crucially, LIME and SHAP Explainable AI (XAI) techniques are integrated to provide transparency by confirming that predictions are based on clinically relevant features like irregular morphology. The system achieved a high overall Accuracy of 92.50 % and a Macro-AUC of 98.82 %, successfully outperforming various prior benchmarked architectures. This work successfully validates a verifiable framework that combines high performance with the essential clinical interpretability required for safe diagnostic deployment. Future research should prioritize enhancing discrimination for critical categories, such as Melanoma NOS (F1-Score is 0.8602).