Interpretable Deep Learning for Skin Cancer Detection: Exploring LIME and SHAP

An effective treatment for skin cancer necessitates early detection, a global problem. This work introduces a unique deep learning technique that extracts important characteristics from skin lesion photos by using CNNs, Resnet50, VGGNet, and DenseNet. The integration of LIME and SHAP approaches improves interpretability by emphasising significant portions of the picture. According to the model’s competitive performance criteria (specificity, sensitivity, and accuracy), dermatologists may be able to depend on it to assist in the early identification of skin cancer. The research also examines the possible advantages and drawbacks of deep learning for the diagnosis of skin cancer, emphasising the technology’s potential to advance healthcare by strengthening diagnostic abilities.