Journal article
Effectiveness of federated learning and CNN ensemble architectures for identifying brain tumors using MRI images
Neural Processing Letters
2022
Abstract
Medical institutions often revoke data access due to the privacy concern of patients. Federated Learning (FL) is a collaborative learning paradigm that can generate an unbiased global model based on collecting updates from local models trained by client’s data while keeping the local data private. This study aims to address the centralized data collection issue through the application of FL on brain tumor identification from MRI images. At first, several CNN models were trained using the MRI data and the best three performing CNN models were selected to form different variants of ensemble classifiers. Afterward, the FL model was constructed using the ensemble architecture. It was trained using model weights from the local model without sharing the client’s data (MRI images) using the FL approach. Experimental results show only a slight decline in the performance of the FL approach as it achieved 91.05% accuracy compared to the 96.68% accuracy of the base ensemble model. Additionally, same approach was taken for another slightly larger dataset to prove the scalability of the method. This study shows that the FL approach can achieve privacy-protected tumor classification from MRI images without compromising much accuracy compared to the traditional deep learning approach.
Details
- Title
- Effectiveness of federated learning and CNN ensemble architectures for identifying brain tumors using MRI images
- Authors/Creators
- M. Islam (Author/Creator) - BRAC UniversityMd.T. Reza (Author/Creator)M. Kaosar (Author/Creator)M.Z. Parvez (Author/Creator)
- Publication Details
- Neural Processing Letters
- Publisher
- Springer Nature
- Identifiers
- 991005541162407891
- Copyright
- © 2022 Springer Nature Switzerland AG. Part of Springer Nature.
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Journal article
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90 Record Views
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.113 Brain Imaging
- 1.113.460 Advanced Neuroimaging
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- ESI research areas
- Computer Science