Retrospective Analysis of an Interpretable Skin Cancer Classification using Deep Learning Models

Skin cancer, particularly malignant melanoma, presents major diagnostic challenges due to similarities with benign tumors. Automatic detection systems based on deep learning algorithms provide intriguing answers, but lack interpretability, limiting clinical application. This research presents an explainable artificial intelligence (XAI) approach to skin lesion classification, with the goal of improving diagnostic accuracy and clinician trust. The created algorithm accurately identifies types of skin lesions using the dataset. Using the local interpretable model-agnostic explanations (LIME) framework, the model creates visual explanations to assist dermatologists in rational diagnosis. Explainability is integrated into the model, increasing its usability in real-world clinical practice. Furthermore, the study emphasizes the importance of XAI in increasing the interpretability and trustworthiness of deep learning models for skin cancer diagnosis, focusing on crucial features such as asymmetry and pigment network classification. This study helps to address the critical need for transparent and accurate AI models in the medical realm, particularly in preventing the rising global prevalence of skin cancer.