Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images
Abstract The most common carcinoma-related cause of death among women is breast cancer. Early detection is crucial, and the manual screening method may lead to a delayed diagnosis, which would delay treatment and put lives at risk. Mammography imaging is advised for routine screening to diagnose bre...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-80187-7 |
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| author | Chhaya Gupta Nasib Singh Gill Preeti Gulia Noha Alduaiji J. Shreyas Piyush Kumar Shukla |
| author_facet | Chhaya Gupta Nasib Singh Gill Preeti Gulia Noha Alduaiji J. Shreyas Piyush Kumar Shukla |
| author_sort | Chhaya Gupta |
| collection | DOAJ |
| description | Abstract The most common carcinoma-related cause of death among women is breast cancer. Early detection is crucial, and the manual screening method may lead to a delayed diagnosis, which would delay treatment and put lives at risk. Mammography imaging is advised for routine screening to diagnose breast cancer at an early stage. To improve generalizability, this study examines the implementation of Federated Learning (FedL) to detect breast cancer. Its performance is compared to a centralized training technique that diagnoses breast cancer. Although FedL has been famous as a safeguarding privacy algorithm, its similarities to ensemble learning methods, such as federated averaging (FEDAvrg), still need to be thoroughly investigated. This study examines explicitly how a YOLOv6 model trained with FedL performs across several clients. A new homomorphic encryption and decryption algorithm is also proposed to retain data privacy. A novel pruned YOLOv6 model with FedL is introduced in this study to differentiate benign and malignant tissues. The model is trained on the breast cancer pathological dataset BreakHis and BUSI. The proposed model achieved a validation accuracy of 98% on BreakHis dataset and 97% on BUSI dataset. The results are compared with the VGG-19, ResNet-50, and InceptionV3 algorithms, showing that the proposed model achieved better results. The tests reveal that federated learning is feasible, as FedAvrg trains models of outstanding quality with only a few communication rounds, as shown by the results on a range of model topologies such as ResNet50, VGG-19, InceptionV3, and the proposed Ensembled FedL YOLOv6. |
| format | Article |
| id | doaj-art-c9a87e2083a94eedb0c6fbe3a250aafb |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-c9a87e2083a94eedb0c6fbe3a250aafb2025-02-02T12:19:59ZengNature PortfolioScientific Reports2045-23222025-01-0115113510.1038/s41598-024-80187-7Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology imagesChhaya Gupta0Nasib Singh Gill1Preeti Gulia2Noha Alduaiji3J. Shreyas4Piyush Kumar Shukla5Department of Computer Science and Applications, Maharshi Dayanand UniversityDepartment of Computer Science and Applications, Maharshi Dayanand UniversityDepartment of Computer Science and Applications, Maharshi Dayanand UniversityDepartment of Computer Science, College of Computer and Information Sciences, Majmaah UniversityDepartment of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher EducationDepartment of Computer Science and Engineering, University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya (State Technological University of Madhya Pradesh)Abstract The most common carcinoma-related cause of death among women is breast cancer. Early detection is crucial, and the manual screening method may lead to a delayed diagnosis, which would delay treatment and put lives at risk. Mammography imaging is advised for routine screening to diagnose breast cancer at an early stage. To improve generalizability, this study examines the implementation of Federated Learning (FedL) to detect breast cancer. Its performance is compared to a centralized training technique that diagnoses breast cancer. Although FedL has been famous as a safeguarding privacy algorithm, its similarities to ensemble learning methods, such as federated averaging (FEDAvrg), still need to be thoroughly investigated. This study examines explicitly how a YOLOv6 model trained with FedL performs across several clients. A new homomorphic encryption and decryption algorithm is also proposed to retain data privacy. A novel pruned YOLOv6 model with FedL is introduced in this study to differentiate benign and malignant tissues. The model is trained on the breast cancer pathological dataset BreakHis and BUSI. The proposed model achieved a validation accuracy of 98% on BreakHis dataset and 97% on BUSI dataset. The results are compared with the VGG-19, ResNet-50, and InceptionV3 algorithms, showing that the proposed model achieved better results. The tests reveal that federated learning is feasible, as FedAvrg trains models of outstanding quality with only a few communication rounds, as shown by the results on a range of model topologies such as ResNet50, VGG-19, InceptionV3, and the proposed Ensembled FedL YOLOv6.https://doi.org/10.1038/s41598-024-80187-7Federated learning (FedL)YOLOv6Breast cancerTransfer learningResNet-50Inception-V3 |
| spellingShingle | Chhaya Gupta Nasib Singh Gill Preeti Gulia Noha Alduaiji J. Shreyas Piyush Kumar Shukla Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images Scientific Reports Federated learning (FedL) YOLOv6 Breast cancer Transfer learning ResNet-50 Inception-V3 |
| title | Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images |
| title_full | Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images |
| title_fullStr | Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images |
| title_full_unstemmed | Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images |
| title_short | Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images |
| title_sort | applying yolov6 as an ensemble federated learning framework to classify breast cancer pathology images |
| topic | Federated learning (FedL) YOLOv6 Breast cancer Transfer learning ResNet-50 Inception-V3 |
| url | https://doi.org/10.1038/s41598-024-80187-7 |
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