Brain Tumor Detection in MRI Based on Federated Learning with YOLOv11
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912262132858880 |
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| author | Monisha, Sheikh Moonwara Anjum Rahman, Ratun |
| author_facet | Monisha, Sheikh Moonwara Anjum Rahman, Ratun |
| contents | One of the primary challenges in medical diagnostics is the accurate and efficient use of magnetic resonance imaging (MRI) for the detection of brain tumors. But the current machine learning (ML) approaches have two major limitations, data privacy and high latency. To solve the problem, in this work we propose a federated learning architecture for a better accurate brain tumor detection incorporating the YOLOv11 algorithm. In contrast to earlier methods of centralized learning, our federated learning approach protects the underlying medical data while supporting cooperative deep learning model training across multiple institutions. To allow the YOLOv11 model to locate and identify tumor areas, we adjust it to handle MRI data. To ensure robustness and generalizability, the model is trained and tested on a wide range of MRI data collected from several anonymous medical facilities. The results indicate that our method significantly maintains higher accuracy than conventional approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04087 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Brain Tumor Detection in MRI Based on Federated Learning with YOLOv11 Monisha, Sheikh Moonwara Anjum Rahman, Ratun Computer Vision and Pattern Recognition Machine Learning One of the primary challenges in medical diagnostics is the accurate and efficient use of magnetic resonance imaging (MRI) for the detection of brain tumors. But the current machine learning (ML) approaches have two major limitations, data privacy and high latency. To solve the problem, in this work we propose a federated learning architecture for a better accurate brain tumor detection incorporating the YOLOv11 algorithm. In contrast to earlier methods of centralized learning, our federated learning approach protects the underlying medical data while supporting cooperative deep learning model training across multiple institutions. To allow the YOLOv11 model to locate and identify tumor areas, we adjust it to handle MRI data. To ensure robustness and generalizability, the model is trained and tested on a wide range of MRI data collected from several anonymous medical facilities. The results indicate that our method significantly maintains higher accuracy than conventional approaches. |
| title | Brain Tumor Detection in MRI Based on Federated Learning with YOLOv11 |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.04087 |