Melanoma Classification Through Deep Ensemble Learning and Explainable AI

TL;DR

This paper presents a deep ensemble learning approach combined with explainable AI techniques to improve melanoma detection accuracy and interpretability, addressing trust issues in AI-based diagnostics.

Contribution

It introduces a novel ensemble of transfer learning models with XAI methods to enhance melanoma classification and explainability.

Findings

High accuracy in melanoma detection

Improved model interpretability with XAI

Enhanced trustworthiness of AI diagnostics

Abstract

Melanoma is one of the most aggressive and deadliest skin cancers, leading to mortality if not detected and treated in the early stages. Artificial intelligence techniques have recently been developed to help dermatologists in the early detection of melanoma, and systems based on deep learning (DL) have been able to detect these lesions with high accuracy. However, the entire community must overcome the explainability limit to get the maximum benefit from DL for diagnostics in the healthcare domain. Because of the black box operation's shortcomings in DL models' decisions, there is a lack of reliability and trust in the outcomes. However, Explainable Artificial Intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This paper proposes a machine learning model using ensemble learning of three state-of-the-art deep transfer Learning networks, along with an approach to ensure the reliability of the predictions by utilizing XAI techniques to explain the basis of the predictions.