Explainable Pre-Trained Models for Skin Cancer Classification

Skin cancers represent a significant health issue, posing considerable challenges in early detection and accurate classification. This study investigated the effectiveness of Deep Learning (DL) models for skin cancers classification using dermoscopic images. Two pre-trained models, DenseNet169 and VGG16, were employed. The DenseNet169 model delivered the highest accuracy, reaching 90.61% while, the VGG16 model achieved an accuracy of 84.24%. The two models were assessed using key performance metrics to conduct a comprehensive evaluation of their effectiveness and overall performance. This analysis provided significant insights on various aspects of each model, allowing a detailed comparison. It also provided a clearer comprehension of the strengths and weaknesses of both models, aiding in informed decision-making and driving further optimization. In addition, three explainable AI (XAI) techniques were applied to deliver perceptions into the decision-making process. These techniques enhance model interpretability by emphasizing the regions of the input images that contribute most to the classification decision, thus improving the transparency and reliability of the model's predictions. Reliable skin cancer classification has the potential to assist healthcare specialists, particularly dermatologists, in making early diagnoses, facilitating timely interventions, and ultimately improving patient outcomes. This study offers valuable contributions for enhancing skin cancer detection and classification, supporting accurate diagnosis and treatment development for medical practitioners.