A Deep Learning Approach for Automated Skin Lesion Diagnosis with Explainable AI

TL;DR

This paper presents a deep learning model for skin lesion classification that achieves high accuracy and incorporates explainable AI techniques to improve transparency and clinical trust.

Contribution

It introduces a hybrid EfficientNetV2-L model with data balancing, augmentation, and a three-stage learning process, combined with XAI methods for interpretability.

Findings

Accuracy of 91.15% achieved

High performance on melanoma and nevi classes

XAI techniques improve diagnostic transparency

Abstract

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.