Federated Learning-Based EfficientNet in Brain Tumor Classification

Baicheng Chen

2024

Abstract

The trend of implementing Machine Learning algorithms in the medical diagnosis field is necessary and meaningful. However, data privacy has become a big problem in applications. This paper uses the Federated Learning (FL) architecture to deal with the privacy problem and finds ways to improve the model’s performance. The study combines the FedAvg FL Algorithm and the CNN model EfficientNet to train the model on the Brain Tumor Classification (MRI) dataset. Before implementing the algorithm, the study did some preprocessing on the data. Then, the study used EfficientNet to further process and recognize the images and FedAvg to weighted average the models trained by clients. Moreover, the study explored the optimizers and loss functions, choosing the AdamW and Cross-entropy loss which fitted this task better. Finally, the study went deep into parameter tuning work, drawing some curves and tables to visualize the results. After parameter tuning, this paper found a nice testing accuracy of 81.218% and a high training accuracy of almost 99% averaged by all the clients. Also, the paper discusses the conditions for implementing different CNN models and analyses their pros and cons in the medical diagnosis field, providing some ideas for the combination of network models and algorithms.

Paper Citation

in Harvard Style

Chen B. (2024). Federated Learning-Based EfficientNet in Brain Tumor Classification. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 458-462. DOI: 10.5220/0012950900004508

in Bibtex Style

@conference{emiti24,
author={Baicheng Chen},
title={Federated Learning-Based EfficientNet in Brain Tumor Classification},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={458-462},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012950900004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Federated Learning-Based EfficientNet in Brain Tumor Classification
SN - 978-989-758-713-9
AU - Chen B.
PY - 2024
SP - 458
EP - 462
DO - 10.5220/0012950900004508
PB - SciTePress