Abstract
The fast proliferation of the coronavirus around the globe has put several countries’ healthcare systems in danger of collapsing. As a result, locating and separating COVID-19-positive patients is a critical task. Deep learning approaches were used in several computer-aided automated systems that utilized chest computed tomography or chest X-ray images to create diagnostic tools. However, current convolutional neural network (CNN) based deep learning algorithms cannot capture the global context because of inherent image-specific inductive bias. These techniques also require large and labeled datasets to train the algorithm, but not many labeled COVID-19 datasets exist publicly. This paper proposes a Federated Learning framework with a Vision Transformer for COVID-19 detection on chest X-ray images to improve training efficiency and accuracy. The transformer architecture can exploit the unlabeled datasets using pre-training, whereas federated learning enables participating clients to jointly train models without disclosing source data outside the originating site. We experimentally establish that our proposed Vision Transformer based Federated Learning architecture outperforms CNN based centralized models. We also provide the characteristics of X-ray images of the COVID-19-affected patients. Our findings show that the proposed model can assist medical professionals in effective COVID-19 screening.
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Acknowledgement
Dr. Sriparna Saha gratefully acknowledges the Young Faculty Research Fellowship (YFRF) Award, supported by Visvesvaraya Ph.D. Scheme for Electronics and IT, Ministry of Electronics and Information Technology (MeitY), Government of India, being implemented by Digital India Corporation (formerly Media Lab Asia) for carrying out this research.
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Sahoo, P., Saha, S., Mondal, S., Chowdhury, S., Gowda, S. (2023). Vision Transformer-Based Federated Learning for COVID-19 Detection Using Chest X-Ray. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_7
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