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Authors: Wadduwage Perera ; Abm Islam ; Van Pham and Min An

Affiliation: Department of Computer Science, Sam Houston State University, Huntsville, Texas, U.S.A.

Keyword(s): Melanoma Classification, Deep Learning, Deep Ensemble Learning, Explainable AI.

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. (More)

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Paper citation in several formats:
Perera, W., Islam, A., Pham, and An, M. (2024). Melanoma Classification Through Deep Ensemble Learning and Explainable AI. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF; ISBN 978-989-758-688-0; ISSN 2184-4305, SciTePress, pages 263-274. DOI: 10.5220/0012575400003657

@conference{healthinf24,
author={Wadduwage Perera and Abm Islam and Van Pham and Min An},
title={Melanoma Classification Through Deep Ensemble Learning and Explainable AI},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF},
year={2024},
pages={263-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012575400003657},
isbn={978-989-758-688-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF
TI - Melanoma Classification Through Deep Ensemble Learning and Explainable AI
SN - 978-989-758-688-0
IS - 2184-4305
AU - Perera, W.
AU - Islam, A.
AU - Pham, V.
AU - An, M.
PY - 2024
SP - 263
EP - 274
DO - 10.5220/0012575400003657
PB - SciTePress

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