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).

翻译：多类别皮肤病变的准确及时诊断受到主观方法、HAM10000等数据集中固有的数据不平衡以及深度学习模型的“黑箱”性质的阻碍。本研究提出了一种可信赖且高精度的计算机辅助诊断系统以克服这些限制。该方法利用深度卷积生成对抗网络进行按类别数据增强，以解决关键的类别不平衡问题。随后在增强数据集上训练微调的ResNet-50分类器，对七种皮肤病类别进行分类。关键的是，通过整合LIME和SHAP可解释人工智能技术，确认预测基于不规则形态等临床相关特征，从而提供透明度。该系统实现了92.50%的高总体准确率和98.82%的宏AUC，成功超越了多种先前基准架构。本工作成功验证了一个可验证的框架，该框架将高性能与安全诊断部署所需的临床可解释性相结合。未来研究应优先提升对关键类别（如非特指型黑色素瘤，其F1分数为0.8602）的区分能力。