Advances in Pneumonia Detection: A Comprehensive Investigation of Federated Learning and Deep Learning-Based Approaches

Bingchen Duan

2024

Abstract

In the realm of healthcare, federated learning (FL) emerges as a promising solution to address the challenges of data silos and privacy concerns in medical diagnosis. This paper delves into the application of FL in the context of pneumonia detection, with a focus on leveraging convolutional neural networks (CNNs) within a federated learning framework. The study provides a comprehensive overview of the potential of FL in processing sensitive medical data, particularly in enhancing the accuracy of pneumonia detection. By employing deep learning models such as Convolutional Neural Networks, VGG-16, ResNet50, and DenseNet121, the research demonstrates significant improvements in detection accuracy. Furthermore, the paper explores the integration of ensemble learning with federated learning, highlighting its potential to augment the generalization capabilities of models while bolstering data privacy protection. Despite the promising results, the study also identifies several key challenges that need to be addressed, including issues related to data quality, communication overhead, evolving healthcare regulations, and the need for standardization in the application of federated learning in healthcare settings. Overall, this paper underscores the potential of federated learning in revolutionizing the diagnosis of pneumonia while ensuring the protection of patient privacy and data security.

Paper Citation

in Harvard Style

Duan B. (2024). Advances in Pneumonia Detection: A Comprehensive Investigation of Federated Learning and Deep Learning-Based Approaches. 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 714-718. DOI: 10.5220/0012969400004508

in Bibtex Style

@conference{emiti24,
author={Bingchen Duan},
title={Advances in Pneumonia Detection: A Comprehensive Investigation of Federated Learning and Deep Learning-Based Approaches},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={714-718},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012969400004508},
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 - Advances in Pneumonia Detection: A Comprehensive Investigation of Federated Learning and Deep Learning-Based Approaches
SN - 978-989-758-713-9
AU - Duan B.
PY - 2024
SP - 714
EP - 718
DO - 10.5220/0012969400004508
PB - SciTePress