Federated Learning Approach for Breast Cancer Detection Based on DCNN
Breast cancer stands as one of the predominant health challenges globally, affecting millions of women every year and necessitating early and accurate detection to optimize patient outcomes. Currently, while deep convolutional neural networks (DCNNs) have shown promise in breast cancer detection, th...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10462116/ |
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| author | Hussain AlSalman Mabrook S. Al-Rakhami Taha Alfakih Mohammad Mehedi Hassan |
| author_facet | Hussain AlSalman Mabrook S. Al-Rakhami Taha Alfakih Mohammad Mehedi Hassan |
| author_sort | Hussain AlSalman |
| collection | DOAJ |
| description | Breast cancer stands as one of the predominant health challenges globally, affecting millions of women every year and necessitating early and accurate detection to optimize patient outcomes. Currently, while deep convolutional neural networks (DCNNs) have shown promise in breast cancer detection, their application is often hampered by privacy concerns associated with sharing patient data and the limitation of training on small, localized datasets. Addressing these challenges, this manuscript introduces an effective federated learning approach tailored for breast cancer detection, leveraging DCNNs across diverse and large datasets without compromising data privacy. Our experimental findings underscore significant advancements in detection accuracy of 98.9% on three large scale datasets which are VINDR-MAMMO, CMMD, and INBREAST. Additionally, we tested the proposed federated learning performance, showcasing the potential of our approach as a robust and privacy-preserving solution for future breast cancer diagnostic strategies. |
| format | Article |
| id | doaj-art-0f6933c1ccbd4c8284bf620d86b46c77 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0f6933c1ccbd4c8284bf620d86b46c772025-02-08T00:00:12ZengIEEEIEEE Access2169-35362024-01-0112401144013810.1109/ACCESS.2024.337465010462116Federated Learning Approach for Breast Cancer Detection Based on DCNNHussain AlSalman0https://orcid.org/0000-0001-8172-4964Mabrook S. Al-Rakhami1https://orcid.org/0000-0001-5343-8370Taha Alfakih2https://orcid.org/0000-0003-0366-5932Mohammad Mehedi Hassan3https://orcid.org/0000-0002-3479-3606Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaBreast cancer stands as one of the predominant health challenges globally, affecting millions of women every year and necessitating early and accurate detection to optimize patient outcomes. Currently, while deep convolutional neural networks (DCNNs) have shown promise in breast cancer detection, their application is often hampered by privacy concerns associated with sharing patient data and the limitation of training on small, localized datasets. Addressing these challenges, this manuscript introduces an effective federated learning approach tailored for breast cancer detection, leveraging DCNNs across diverse and large datasets without compromising data privacy. Our experimental findings underscore significant advancements in detection accuracy of 98.9% on three large scale datasets which are VINDR-MAMMO, CMMD, and INBREAST. Additionally, we tested the proposed federated learning performance, showcasing the potential of our approach as a robust and privacy-preserving solution for future breast cancer diagnostic strategies.https://ieeexplore.ieee.org/document/10462116/Breast cancer detectionfederated learningdeep convolutional neural networksDCNNmedical image analysishealthcare data privacy |
| spellingShingle | Hussain AlSalman Mabrook S. Al-Rakhami Taha Alfakih Mohammad Mehedi Hassan Federated Learning Approach for Breast Cancer Detection Based on DCNN IEEE Access Breast cancer detection federated learning deep convolutional neural networks DCNN medical image analysis healthcare data privacy |
| title | Federated Learning Approach for Breast Cancer Detection Based on DCNN |
| title_full | Federated Learning Approach for Breast Cancer Detection Based on DCNN |
| title_fullStr | Federated Learning Approach for Breast Cancer Detection Based on DCNN |
| title_full_unstemmed | Federated Learning Approach for Breast Cancer Detection Based on DCNN |
| title_short | Federated Learning Approach for Breast Cancer Detection Based on DCNN |
| title_sort | federated learning approach for breast cancer detection based on dcnn |
| topic | Breast cancer detection federated learning deep convolutional neural networks DCNN medical image analysis healthcare data privacy |
| url | https://ieeexplore.ieee.org/document/10462116/ |
| work_keys_str_mv | AT hussainalsalman federatedlearningapproachforbreastcancerdetectionbasedondcnn AT mabrooksalrakhami federatedlearningapproachforbreastcancerdetectionbasedondcnn AT tahaalfakih federatedlearningapproachforbreastcancerdetectionbasedondcnn AT mohammadmehedihassan federatedlearningapproachforbreastcancerdetectionbasedondcnn |