An Explainable Deep Learning Framework for Early Skin Cancer Detection Using Dermoscopic Image Analysis
TL;DR
Experimental results indicate that the proposed framework achieves high diagnostic accuracy while providing interpretable insights that assist dermatologists in understanding model predictions.
Abstract
Skin cancer is one of the most prevalent and potentially life-threatening diseases worldwide. Early detection is crucial for improving treatment outcomes and increasing patient survival rates. This study proposes an Explainable Deep Learning Framework for Early Skin Cancer Detection using Dermoscopic Image Analysis. The framework utilizes deep convolutional neural networks (CNNs) to analyze dermoscopic images and accurately classify skin lesions as benign or malignant. To enhance trust, transparency, and clinical usability, Explainable Artificial Intelligence (XAI) techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) are incorporated to highlight the regions of the image that influence the model’s decisions. The system includes image preprocessing, data augmentation, feature extraction, lesion classification, and visual explanation modules. Experimental results indicate that the proposed framework achieves high diagnostic accuracy while providing interpretable insights that assist dermatologists in understanding model predictions. The integration of deep learning and explainable AI supports reliable computer-aided diagnosis, reduces diagnostic uncertainty, and promotes the adoption of intelligent healthcare technologies for early skin cancer screening.