Abstract
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).