用深度学习自动诊断皮肤病变，结果可解释且准确率超91%
A Deep Learning Approach for Automated Skin Lesion Diagnosis with Explainable AI
- 融合数据增强与通道注意力的EfficientNetV2-L模型
- 在HAM10000数据集上准确率达91.15%，AUC达99.33%
- 通过可视化技术揭示判别依据，提升临床可信度
皮肤癌是全球最常见且危险的癌症之一，需及时精准诊断。本文提出一种针对HAM10000数据集的多类皮肤病变分类深度学习架构，结合高质量数据平衡、大规模数据增强、混合EfficientNetV2-L框架及通道注意力机制，并采用三阶段渐进式学习策略。同时引入可解释AI（XAI）技术如Grad-CAM和显著性图，生成模型预测的可视解释。整体准确率达91.15%，宏F1为85.45%，微平均AUC为99.33%。模型在七类病变中表现优异，尤其对黑色素瘤和黑素细胞痣识别效果突出。XAI不仅提升诊断透明度，还揭示关键视觉特征，增强临床信任。
原文摘要 · Abstract (English)
Skin cancer is also one of the most common and dangerous types of cancer in the world that requires timely and precise diagnosis. In this paper, a deep-learning architecture of the multi-class skin lesion classification on the HAM10000 dataset will be described. The system suggested combines high-quality data balancing methods, large-scale data augmentation, hybridized EfficientNetV2-L framework with channel attention, and a three-stage progressive learning approach. Moreover, we also use explainable AI (XAI) techniques such as Grad-CAM and saliency maps to come up with intelligible visual representations of model predictions. Our strategy is with a total accuracy of 91.15 per cent, macro F1 of 85.45\% and micro-average AUC of 99.33\%. The model has shown high performance in all the seven lesion classes with specific high performance of melanoma and melanocytic nevi. In addition to enhancing diagnostic transparency, XAI also helps to find out the visual characteristics that cause the classifications, which enhances clinical trustworthiness.
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