Version 1
: Received: 4 September 2024 / Approved: 5 September 2024 / Online: 5 September 2024 (11:00:11 CEST)
How to cite:
Mirasbekov, Y.; Aidossov, N.; Mashekova, A.; Zarikas, V.; Zhao, Y.; Ng, E. Y. K.; Midlenko, A. Further Development and Validation of an Interpretable Deep Learning Model for IR Image Based Breast Cancer Diagnosis. Preprints2024, 2024090404. https://doi.org/10.20944/preprints202409.0404.v1
Mirasbekov, Y.; Aidossov, N.; Mashekova, A.; Zarikas, V.; Zhao, Y.; Ng, E. Y. K.; Midlenko, A. Further Development and Validation of an Interpretable Deep Learning Model for IR Image Based Breast Cancer Diagnosis. Preprints 2024, 2024090404. https://doi.org/10.20944/preprints202409.0404.v1
Mirasbekov, Y.; Aidossov, N.; Mashekova, A.; Zarikas, V.; Zhao, Y.; Ng, E. Y. K.; Midlenko, A. Further Development and Validation of an Interpretable Deep Learning Model for IR Image Based Breast Cancer Diagnosis. Preprints2024, 2024090404. https://doi.org/10.20944/preprints202409.0404.v1
APA Style
Mirasbekov, Y., Aidossov, N., Mashekova, A., Zarikas, V., Zhao, Y., Ng, E. Y. K., & Midlenko, A. (2024). Further Development and Validation of an Interpretable Deep Learning Model for IR Image Based Breast Cancer Diagnosis. Preprints. https://doi.org/10.20944/preprints202409.0404.v1
Chicago/Turabian Style
Mirasbekov, Y., Eddie Yin Kwee Ng and Anna Midlenko. 2024 "Further Development and Validation of an Interpretable Deep Learning Model for IR Image Based Breast Cancer Diagnosis" Preprints. https://doi.org/10.20944/preprints202409.0404.v1
Abstract
Breast cancer remains a global health problem requiring effective diagnostic methods for early detection in order to achieve WHO’s ultimate goal of breast self-examination (BSE). A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, and non-invasive and adjunctive and complementary detection method. This research explores the potential of using machine learning (ML) techniques, specifically Bayesian Networks (BN) combined with Convolutional Neural Networks (CNN) to improve breast cancer diagnosis at early stages. Explainable artificial intelligence (XAI) aims to clarify the reasoning behind any output of artificial neural networks based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We have constructed two diagnostic expert models. In research model A, combining thermal images after XAI process together with medical records, an accuracy of 84.07% has been achieved, while model B, that includes also CNN prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of XAI to transform breast cancer diagnosis, increasing accuracy and reducing the risk of misdiagnosis
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
