A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework
Breast cancer continues to pose a substantial worldwide public health concern, necessitating the use of sophisticated diagnostic methods to enable timely identification and management. The present research utilizes an iterative methodology for collaborative learning, using Deep Neural Networks (DNN)...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Healthcare |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9032/11/24/3185 |
| _version_ | 1797380825867616256 |
|---|---|
| author | Maram Fahaad Almufareh Noshina Tariq Mamoona Humayun Bushra Almas |
| author_facet | Maram Fahaad Almufareh Noshina Tariq Mamoona Humayun Bushra Almas |
| author_sort | Maram Fahaad Almufareh |
| collection | DOAJ |
| description | Breast cancer continues to pose a substantial worldwide public health concern, necessitating the use of sophisticated diagnostic methods to enable timely identification and management. The present research utilizes an iterative methodology for collaborative learning, using Deep Neural Networks (DNN) to construct a breast cancer detection model with a high level of accuracy. By leveraging Federated Learning (FL), this collaborative framework effectively utilizes the combined knowledge and data assets of several healthcare organizations while ensuring the protection of patient privacy and data security. The model described in this study showcases significant progress in the field of breast cancer diagnoses, with a maximum accuracy rate of 97.54%, precision of 96.5%, and recall of 98.0%, by using an optimum feature selection technique. Data augmentation approaches play a crucial role in decreasing loss and improving model performance. Significantly, the F1-Score, a comprehensive metric for evaluating performance, turns out to be 97%. This study signifies a notable advancement in the field of breast cancer screening, fostering hope for improved patient outcomes via increased accuracy and reliability. This study highlights the potential impact of collaborative learning, namely, in the field of FL, in transforming breast cancer detection. The incorporation of privacy considerations and the use of diverse data sources contribute to the advancement of early detection and the treatment of breast cancer, hence yielding significant benefits for patients on a global scale. |
| first_indexed | 2024-03-08T20:43:39Z |
| format | Article |
| id | doaj.art-441762b8576d4ecf8d9c052bbf4b061e |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-08T20:43:39Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-441762b8576d4ecf8d9c052bbf4b061e2023-12-22T14:12:04ZengMDPI AGHealthcare2227-90322023-12-011124318510.3390/healthcare11243185A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning FrameworkMaram Fahaad Almufareh0Noshina Tariq1Mamoona Humayun2Bushra Almas3Department of Information Systems, College of Computer and Information Sciences, Jouf University, Al Jouf 72311, Saudi ArabiaDepartment of Avionics Engineering, Air University, Islamabad 44000, PakistanDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Al Jouf 72311, Saudi ArabiaInstitute of Information Technology, Quaid-i-Azam University, Islamabad 45320, PakistanBreast cancer continues to pose a substantial worldwide public health concern, necessitating the use of sophisticated diagnostic methods to enable timely identification and management. The present research utilizes an iterative methodology for collaborative learning, using Deep Neural Networks (DNN) to construct a breast cancer detection model with a high level of accuracy. By leveraging Federated Learning (FL), this collaborative framework effectively utilizes the combined knowledge and data assets of several healthcare organizations while ensuring the protection of patient privacy and data security. The model described in this study showcases significant progress in the field of breast cancer diagnoses, with a maximum accuracy rate of 97.54%, precision of 96.5%, and recall of 98.0%, by using an optimum feature selection technique. Data augmentation approaches play a crucial role in decreasing loss and improving model performance. Significantly, the F1-Score, a comprehensive metric for evaluating performance, turns out to be 97%. This study signifies a notable advancement in the field of breast cancer screening, fostering hope for improved patient outcomes via increased accuracy and reliability. This study highlights the potential impact of collaborative learning, namely, in the field of FL, in transforming breast cancer detection. The incorporation of privacy considerations and the use of diverse data sources contribute to the advancement of early detection and the treatment of breast cancer, hence yielding significant benefits for patients on a global scale.https://www.mdpi.com/2227-9032/11/24/3185federated learningbreast cancerpredictioncollaborative learningdeep neural networks |
| spellingShingle | Maram Fahaad Almufareh Noshina Tariq Mamoona Humayun Bushra Almas A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework Healthcare federated learning breast cancer prediction collaborative learning deep neural networks |
| title | A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework |
| title_full | A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework |
| title_fullStr | A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework |
| title_full_unstemmed | A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework |
| title_short | A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework |
| title_sort | federated learning approach to breast cancer prediction in a collaborative learning framework |
| topic | federated learning breast cancer prediction collaborative learning deep neural networks |
| url | https://www.mdpi.com/2227-9032/11/24/3185 |
| work_keys_str_mv | AT maramfahaadalmufareh afederatedlearningapproachtobreastcancerpredictioninacollaborativelearningframework AT noshinatariq afederatedlearningapproachtobreastcancerpredictioninacollaborativelearningframework AT mamoonahumayun afederatedlearningapproachtobreastcancerpredictioninacollaborativelearningframework AT bushraalmas afederatedlearningapproachtobreastcancerpredictioninacollaborativelearningframework AT maramfahaadalmufareh federatedlearningapproachtobreastcancerpredictioninacollaborativelearningframework AT noshinatariq federatedlearningapproachtobreastcancerpredictioninacollaborativelearningframework AT mamoonahumayun federatedlearningapproachtobreastcancerpredictioninacollaborativelearningframework AT bushraalmas federatedlearningapproachtobreastcancerpredictioninacollaborativelearningframework |