Advancing Lung Cancer Diagnosis: Federated Learning-Based Privacy Innovations

Zixiang Hao

2024

Abstract

healthcare systems. In recent years, the application of federated learning in lung cancer treatment has gained traction, offering several advantages. Federated learning addresses concerns regarding data privacy and security by allowing local model training on patient data, thereby minimizing the risk of privacy breaches. Furthermore, it facilitates the inclusion of diverse datasets from various healthcare institutions, enabling more comprehensive and representative model training. By analysing and summarizing the three methods—the Federated Learning (FL) + Neural Network (NN) technique (the FL+NN technique), the convolutional IT-2 fuzzy rough federated learning-neural architecture search model (the CIT2FR-FL-NAS model), and U-Net, the article underscores the potential of federated learning to revolutionize lung cancer therapy. The FL+NN technique combines federated learning with neural network models, demonstrating high accuracy in lung cancer classification. The CIT2FR-FL-NAS model integrates federated learning, neural architecture search, and fuzzy rough set theory to achieve accurate classification results while safeguarding privacy and reducing network complexity. Similarly, U-Net, a fully convolutional network architecture, shows effectiveness in segmenting organs in medical imaging, such as the heart and lungs. The potential is shown by the ability of enhancing accuracy, privacy, and collaboration in medical data analysis and treatment planning. The objective of the article is to stimulate further research and innovation in this critical healthcare domain.

Paper Citation

in Harvard Style

Hao Z. (2024). Advancing Lung Cancer Diagnosis: Federated Learning-Based Privacy Innovations. 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 399-403. DOI: 10.5220/0012938800004508

in Bibtex Style

@conference{emiti24,
author={Zixiang Hao},
title={Advancing Lung Cancer Diagnosis: Federated Learning-Based Privacy Innovations},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={399-403},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012938800004508},
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 - Advancing Lung Cancer Diagnosis: Federated Learning-Based Privacy Innovations
SN - 978-989-758-713-9
AU - Hao Z.
PY - 2024
SP - 399
EP - 403
DO - 10.5220/0012938800004508
PB - SciTePress