Melanoma is the most lethal subtype of skin cancer, and early and accurate detection of this disease can greatly improve patients' outcomes. Although machine learning models, especially convolutional neural networks (CNNs), have shown great potential in automating melanoma classification, their diagnostic reliability still suffers due to inconsistent focus on lesion areas. In this study, we analyze the relationship between lesion attention and diagnostic performance, involving masked images, bounding box detection, and transfer learning. We used multiple explainability and sensitivity analysis approaches to investigate how well models aligned their attention with lesion areas and how this alignment correlated with precision, recall, and F1-score. Results showed that models with a higher focus on lesion areas achieved better diagnostic performance, suggesting the potential of interpretable AI in medical diagnostics. This study provides a foundation for developing more accurate and trustworthy melanoma classification models in the future.

翻译：黑色素瘤是皮肤癌中最致命的亚型，早期准确检测该疾病可显著改善患者预后。尽管机器学习模型，特别是卷积神经网络（CNN），在自动化黑色素瘤分类方面展现出巨大潜力，但由于对病灶区域的关注不一致，其诊断可靠性仍存在不足。本研究通过掩码图像、边界框检测和迁移学习等方法，分析了病灶注意力与诊断性能之间的关系。我们采用多种可解释性和敏感性分析方法，探究模型注意力与病灶区域的对齐程度，以及这种对齐如何与精确率、召回率和F1分数相关联。结果表明，对病灶区域关注度更高的模型能获得更好的诊断性能，这揭示了可解释AI在医学诊断中的潜力。本研究为未来开发更准确、更可靠的黑色素瘤分类模型奠定了基础。