用GAN增广数据+XAI解释，提升皮肤病分类准确率与可解释性。
XAI-Driven Skin Disease Classification: Leveraging GANs to Augment ResNet-50 Performance
- 用DCGAN为每类皮肤病生成新样本，解决数据不平衡问题。
- 融合微调的ResNet-50，实现92.50%准确率与98.82%宏AUC。
- 结合LIME/SHAP解释模型决策，确保临床特征可追溯。
多类皮肤病变的精准及时诊断受限于主观判断、如HAM10000数据集中的固有数据不平衡，以及深度学习模型的“黑箱”特性。本研究提出一种可信且高精度的计算机辅助诊断（CAD）系统以克服上述局限。方法采用深度卷积生成对抗网络（DCGAN）对每一类别进行数据增强，缓解关键类别的数据不足问题；随后在增强数据集上训练微调的ResNet-50分类器，实现七类皮肤疾病的分类。至关重要的是，引入LIME与SHAP可解释人工智能（XAI）技术，确保预测基于不规则形态等临床相关特征。系统取得92.50%的整体准确率与98.82%的宏AUC，显著优于多种基准架构。该工作验证了一个兼具高性能与临床可解释性的可验证框架，适用于安全诊断部署。未来研究应重点提升对关键类别（如未特指的黑色素瘤，其F1分数为0.8602）的区分能力。
原文摘要 · Abstract (English)
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).
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