Melanoma Classification Through Deep Ensemble Learning and Explainable AI
This work addresses the need for explainable AI in healthcare diagnostics to enhance trust for dermatologists, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of improving reliability and trust in melanoma detection by combining deep ensemble learning with explainable AI techniques to interpret predictions, achieving high accuracy in classification.
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.