Brain Tumor Classification Based on Federated Learning

Al-Asfoor, Muntasir, Abed, Mohammed Hamzah and Maher, Kevin (2024) Brain Tumor Classification Based on Federated Learning. In: 10th International Conference on Optimization and Applications (ICOA).

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Abstract

This paper investigates the challenges of brain tumor classification and focuses on diagnostic performance while preserving the patient's privacy by investigating decentralized using federated learning. Brain tumor are considered one of the significant health risks and a widespread cancer disease, and the more accurate classification methods will help to diagnose the disease and for effective treatment planning. Conventional methods encounter many difficulties because of the limited availability of diverse medical imaging data as well as privacy regulations. To address these challenges, this method allows for decentralised training across multiple data centres by using the Federated Learning method. The proposed method utilises a pre-trained DenseNet model within an FL environment. This approach guarantees effective feature selection, improving the overall performance of the classification model. The training data remains localised at each node, and only the trainable weights and model updates are shared, therefore preserving data confidentiality. The FL model collects these updates to build a model that is capable of classifying MRI images. The study assesses the efficacy of the FL model through the utilisation of three publicly accessible MRI datasets, thereby substantiating a notable enhancement in classification accuracy when compared to single-institution models. The results indicate that the FL approach not only enhances diagnostic accuracy but also facilitates multi-institutional collaborations without compromising patient data privacy. This solution holds promise for widespread clinical adoption, enabling better management and treatment of brain tumors through advanced, privacy-preserving AI techniques. © 2024 IEEE.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Cited by: 0; Conference name: 10th International Conference on Optimization and Applications, ICOA 2024; Conference date: 17 October 2024 through 18 October 2024; Conference code: 204346
Keywords:	Data privacy, Diagnosis, Diseases, Brain tumor classifications, Brain tumors, Cancer disease, Classification methods, Conventional methods, Decentralised, Diagnostic performance, Imaging data, Patient privacies, Treatment planning, Differential privacy
Depositing User:	RED Unit Admin
Date Deposited:	13 Mar 2025 12:20
Last Modified:	13 Mar 2025 12:22
URI:	https://bnu.repository.guildhe.ac.uk/id/eprint/19670

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