Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases
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 thermograph...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Biomimetics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-7673/9/10/609 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850205909395963904 |
|---|---|
| author | Yerken Mirasbekov Nurduman Aidossov Aigerim Mashekova Vasilios Zarikas Yong Zhao Eddie Yin Kwee Ng Anna Midlenko |
| author_facet | Yerken Mirasbekov Nurduman Aidossov Aigerim Mashekova Vasilios Zarikas Yong Zhao Eddie Yin Kwee Ng Anna Midlenko |
| author_sort | Yerken Mirasbekov |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-52bd20324dad4487b3c27ef8b6d366dc |
| institution | OA Journals |
| issn | 2313-7673 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-52bd20324dad4487b3c27ef8b6d366dc2025-08-20T02:10:58ZengMDPI AGBiomimetics2313-76732024-10-0191060910.3390/biomimetics9100609Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer CasesYerken Mirasbekov0Nurduman Aidossov1Aigerim Mashekova2Vasilios Zarikas3Yong Zhao4Eddie Yin Kwee Ng5Anna Midlenko6School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, KazakhstanSchool of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, KazakhstanSchool of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, KazakhstanDepartment of Mathematics, University of Thessaly, GR-35100 Lamia, GreeceSchool of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, KazakhstanSchool of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, SingaporeSchool of Medicine, Nazarbayev University, Astana 010000, KazakhstanBreast 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.https://www.mdpi.com/2313-7673/9/10/609breast cancerBayesian networksconvolutional neural networksexplainable artificial intelligencemachine learningthermography |
| spellingShingle | Yerken Mirasbekov Nurduman Aidossov Aigerim Mashekova Vasilios Zarikas Yong Zhao Eddie Yin Kwee Ng Anna Midlenko Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases Biomimetics breast cancer Bayesian networks convolutional neural networks explainable artificial intelligence machine learning thermography |
| title | Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases |
| title_full | Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases |
| title_fullStr | Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases |
| title_full_unstemmed | Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases |
| title_short | Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases |
| title_sort | fully interpretable deep learning model using ir thermal images for possible breast cancer cases |
| topic | breast cancer Bayesian networks convolutional neural networks explainable artificial intelligence machine learning thermography |
| url | https://www.mdpi.com/2313-7673/9/10/609 |
| work_keys_str_mv | AT yerkenmirasbekov fullyinterpretabledeeplearningmodelusingirthermalimagesforpossiblebreastcancercases AT nurdumanaidossov fullyinterpretabledeeplearningmodelusingirthermalimagesforpossiblebreastcancercases AT aigerimmashekova fullyinterpretabledeeplearningmodelusingirthermalimagesforpossiblebreastcancercases AT vasilioszarikas fullyinterpretabledeeplearningmodelusingirthermalimagesforpossiblebreastcancercases AT yongzhao fullyinterpretabledeeplearningmodelusingirthermalimagesforpossiblebreastcancercases AT eddieyinkweeng fullyinterpretabledeeplearningmodelusingirthermalimagesforpossiblebreastcancercases AT annamidlenko fullyinterpretabledeeplearningmodelusingirthermalimagesforpossiblebreastcancercases |