Malignant melanoma is one of the most dreadful skin cancer types caused by the abnormal growth of melanocyte cells. Deep convolutional neural network (CNN) models are becoming prominent for the automated diagnosis of melanoma from dermoscopic images. Although being incredibly accurate, the “black-box” nature of deep CNN models due to the lack of proper interpretability still prevents their wide-spread use in clinical settings. This paper proposes an explainable CNN-based stacked ensemble framework to detect melanoma skin cancer at earlier stages. In the stacking ensemble framework, the transfer learning concept is used where multiple CNN sub-models that perform the same classification task are assembled. A new model called a meta-learner uses all the sub-models’ predictions and generates the final prediction results. The model is evaluated using an open-access dataset containing both benign and malignant melanoma images. An explainability method is developed by shapely adaptive explanations to produce heatmaps that visualize the areas of melanoma images that are most indicative of the disease. This provides interpretability of our model’s decision in a manner understandable to dermatologists. Evaluation results show the effectiveness of our ensemble model with a high degree of accuracy (95.76%), sensitivity (96.67%), and AUC (0.957).

恶性黑色素瘤是由黑色素细胞异常生长引起的最可怕的皮肤癌类型之一。深度卷积神经网络（CNN）模型对于从皮肤镜图像自动诊断黑色素瘤正变得越来越重要。尽管非常准确，但由于缺乏适当的解释性，深层CNN模型的“黑匣子”性质仍然阻止了它们在临床环境中的广泛使用。本文提出了一种可解释的基于CNN的堆叠整体框架，可以在早期阶段检测黑色素瘤皮肤癌。在堆叠集成框架中，使用转移学习概念，其中将执行相同分类任务的多个CNN子模型组装在一起。一个称为元学习器的新模型使用所有子模型的预测并生成最终的预测结果。使用包含良性和恶性黑色素瘤图像的开放式数据集对模型进行评估。通过形状适应性解释开发了一种可解释性方法，以产生热图，该热图可视化最能表明该病的黑素瘤图像区域。这以皮肤科医生可以理解的方式提供了我们模型决策的可解释性。评估结果显示了我们的集成模型的有效性，该模型具有较高的准确度（95.76％），灵敏度（96.67％）和AUC（0.957）。这以皮肤科医生可以理解的方式提供了我们模型决策的可解释性。评估结果显示了我们的集成模型的有效性，该模型具有较高的准确度（95.76％），灵敏度（96.67％）和AUC（0.957）。这以皮肤科医生可以理解的方式提供了我们模型决策的可解释性。评估结果显示了我们的集成模型的有效性，该模型具有较高的准确度（95.76％），灵敏度（96.67％）和AUC（0.957）。