A Deep Learning Approach for Automated Skin Lesion Diagnosis with Explainable AI
This addresses the need for timely and precise skin cancer diagnosis for clinicians, though it is incremental as it builds on existing methods with hybrid improvements.
The paper tackles automated skin lesion diagnosis by developing a deep learning system that achieves 91.15% accuracy, 85.45% macro F1, and 99.33% micro-average AUC on the HAM10000 dataset, with explainable AI techniques to enhance transparency.
Skin cancer is also one of the most common and dangerous types of cancer in the world that requires timely and precise diagnosis. In this paper, a deep-learning architecture of the multi-class skin lesion classification on the HAM10000 dataset will be described. The system suggested combines high-quality data balancing methods, large-scale data augmentation, hybridized EfficientNetV2-L framework with channel attention, and a three-stage progressive learning approach. Moreover, we also use explainable AI (XAI) techniques such as Grad-CAM and saliency maps to come up with intelligible visual representations of model predictions. Our strategy is with a total accuracy of 91.15 per cent, macro F1 of 85.45\% and micro-average AUC of 99.33\%. The model has shown high performance in all the seven lesion classes with specific high performance of melanoma and melanocytic nevi. In addition to enhancing diagnostic transparency, XAI also helps to find out the visual characteristics that cause the classifications, which enhances clinical trustworthiness.