Advancements and Applications of Using Federated Learning in Diagnosing and Analyzing Brain Tumor Images

Yusong Yang

2024

Abstract

Brain tumors represent a major health concern, and conventional diagnostic approaches can be intricate and prone to errors, especially given the diversity of tumor types. The swift progress in Artificial Intelligence (AI) has emerged as a promising avenue for enhancing brain tumor diagnosis. Federated Learning (FL), which is a distributed machine learning approach, facilitates collaborative model training among various institutions, improving diagnostic precision while safeguarding data privacy. This study introduces a federated learning framework for classifying brain tumors using Convolutional Neural Networks (CNNs), specifically leveraging an optimized Visual Geometry Group 16 (VGG16) architecture alongside transfer learning methods. The model was trained across several clients, achieving an outstanding classification accuracy of 98%. Furthermore, U-Net was utilized for segmenting brain tumors, demonstrating notable performance enhancements with an increasing number of participating clients. Despite the evident advantages of FL regarding privacy preservation and model efficacy, challenges such as differences in institutional equipment and the Non-independent and Identically Distributed (non-IID) characteristics of data impede generalization and convergence of models. To address these challenges, this paper suggests employing adaptive algorithms and data augmentation strategies to improve model flexibility and effectiveness. Additionally, effectively merging multimodal data remains a significant technical challenge that needs resolution in future work.

Paper Citation

in Harvard Style

Yang Y. (2024). Advancements and Applications of Using Federated Learning in Diagnosing and Analyzing Brain Tumor Images. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 557-561. DOI: 10.5220/0013528300004619

in Bibtex Style

@conference{daml24,
author={Yusong Yang},
title={Advancements and Applications of Using Federated Learning in Diagnosing and Analyzing Brain Tumor Images},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={557-561},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013528300004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Advancements and Applications of Using Federated Learning in Diagnosing and Analyzing Brain Tumor Images
SN - 978-989-758-754-2
AU - Yang Y.
PY - 2024
SP - 557
EP - 561
DO - 10.5220/0013528300004619
PB - SciTePress