Skin Cancer Detection Using Preprocessed Vision Transformers: An Interpretability-Driven Approach
Ananya Atri · Aug 15, 2026
Abstract : Skin cancer is an issue affecting health all over the world, and proper diagnosis of the issue would lead to better patient outcomes. Deep Learning, in particular Convolutional Neural Networks (CNNs), has brought more objective and fast screening techniques. This paper assesses the diagnostic capability of Vision Transformers (ViT) with Explainable AI (XAI) on HAM10000 data (10,015 pigmented skin lesion images). The researchers used a critically important preprocessing pipeline comprising of morphology transformations to remove hair, image segmentation to identify regions of interest and data augmentation to overcome imbalanced classes. Five XAI techniques were used, namely, Grad-CAM, LIME, SHAP, Integrated Gradients and Saliency Maps to improve diagnostic transparency. The experiment outcomes revealed that ViT + Grad-CAM architecture was the best performer with a total accuracy of 96.6%. The model achieved 0.92 precision, 0.90 recall and 0.92 F1-score. Grad-CAM was especially useful in a clinical setting as it is fast to run and localizes classes accurately.