Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases
Mirasbekov Y. Aidossov N. Mashekova A. Zarikas V. Zhao Y. Ng E.Y.K. Midlenko A.
October 2024Multidisciplinary Digital Publishing Institute (MDPI)
Biomimetics
2024#9Issue 10
Breast cancer remains a global health problem requiring effective diagnostic methods for early detection, in order to achieve the World Health Organization’s ultimate goal of breast self-examination. A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, non-invasive, adjunctive, and complementary detection method. This research explores the potential of using machine learning techniques, specifically Bayesian networks combined with convolutional neural networks, to improve possible breast cancer diagnosis at early stages. Explainable artificial intelligence aims to clarify the reasoning behind any output of artificial neural network-based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We constructed two diagnostic expert models: Model A and Model B. In this research, Model A, combining thermal images after the explainable artificial intelligence process together with medical records, achieved an accuracy of 84.07%, while model B, which also includes a convolutional neural network prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of explainable artificial intelligence to improve possible breast cancer diagnosis, with very high accuracy.
Bayesian networks , breast cancer , convolutional neural networks , explainable artificial intelligence , machine learning , thermography
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School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Mathematics, University of Thessaly, Lamia, GR-35100, Greece
Mathematical Sciences Research Laboratory (MSRL), Lamia, GR-35100, Greece
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore
School of Medicine, Nazarbayev University, Astana, 010000, Kazakhstan
School of Engineering and Digital Sciences
Department of Mathematics
Mathematical Sciences Research Laboratory (MSRL)
School of Mechanical and Aerospace Engineering
School of Medicine
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