The skin lesion types result in delayed diagnosis due to high similarity in early stages of the skin cancer. In this regard, deep learning algorithms are well-recognized solutions; however, these black box approaches result in lack of trust as dermatologists are unable to interpret and validate the decisions made by the models. In this paper, an explainable artificial intelligence (XAI) based skin lesion classification system is proposed to improve the skin lesion classification accuracy. This will help the dermatologists to make rational diagnosis in the early stages of skin cancer. The proposed XAI model is validated using International Skin Imaging Collaboration (ISIC) 2019 dataset. The developed model correctly identifies the eight types of skin lesions (dermatofibroma, squamous cell carcinoma, benign keratosis, melanocytic nevus, vascular lesion, actinic keratosis, basal cell carcinoma and melanoma) with classification accuracy, precision, recall and F1 score as 94.47%, 93.57%, 94.01%, and 94.45% respectively. These predictions are further analyzed using the local interpretable model-agnostic explanations (LIME) framework to generate visual explanations that match a prior belief and general explanation best practices. The explainability integrated within our model will enhance its applicability in real clinical practice.
Nigar et al. (2022) studied this question.