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

2025-11-29

AI Summary

To address three key challenges in multi-class skin lesion diagnosis—subjectivity in clinical assessment, severe class imbalance (e.g., in the HAM10000 dataset), and limited interpretability of deep learning models—this study proposes a trustworthy AI-assisted diagnostic system. Methodologically, it introduces a DCGAN-driven data augmentation strategy to achieve class-balanced training; employs a fine-tuned ResNet-50 backbone for high-accuracy classification; and pioneers the synergistic integration of LIME and SHAP for dual-path, multimodal explainable AI (XAI), enabling transparent, human-interpretable decision rationale visualization. Evaluated on public benchmarks, the system achieves 92.50% accuracy and 98.82% macro-AUC, with a melanoma-specific F1-score of 0.8602—outperforming state-of-the-art approaches. This work demonstrates the feasibility of combining generative data augmentation with multimodal XAI to enhance both reliability and clinical acceptability of dermatological AI systems.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Large pretrained models with web data

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).

Problem

• Develops a trustworthy CAD system for multi-class skin lesion diagnosis
• Addresses data imbalance using DCGANs for per-class augmentation
• Integrates XAI techniques for model transparency and clinical interpretability

Innovation

• DCGANs augment data to resolve class imbalance
• Fine-tuned ResNet-50 classifies seven skin diseases
• LIME and SHAP provide transparent clinical feature explanations