The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area has proven to yield similar accuracy levels to those obtained by dermatologists in different studies. Nevertheless, many challenges are still present, including underperformance and poor generalization in some cases, as well as low interpretability related to the use of black box models. This creates major obstacles for practical implementation since it requires explainability in addition to accurate diagnostics, making it necessary to find a solution. To overcome the mentioned difficulties, an explainable deep learning framework for skin lesion classification (EDLF-SLC) is developed within this study. The framework makes use of various approaches in machine learning and explainable artificial intelligence (XAI) to ensure improvements in terms of both accuracy and interpretability in a three-stage manner. In the initial stage, deep representations extracted from multiple pretrained CNN architectures are fused to preserve complementary discriminative information learned by different network architectures before classification using an SVM with a radial basis function kernel. Next, Support Vector Machine (SVM) classifiers with a radial basis function kernel are used to classify the obtained features. Finally, the predictions made by the model are interpreted through local interpretable model-agnostic explanations (LIME). Experimental results show that EDLF-SLC reaches 88.0% in accuracy, 89.0% in precision, 87.0% in recall, and 88.0% in F1 score, demonstrating competitive performance compared with several recently reported methods under the experimental conditions considered in this study.
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