COVIDTran: an automated COVID-19 diagnosis system via Context Transfer Transformer
Pages 122 - 126
Abstract
In this paper we present COVIDTran, an automated COVID diagnostic system that takes symptomatic cough audios as input and identifies potential cases of COVID19. Adopting principles from Transfer Learning, we implement neural network based on Vision Transformer that processes the spectrographic maps of the cough audio signals, and promote the robustness of our model by integrating contextual information from similar flu symptomatic datasets via transfer learning. Experimental results involving crowdsourced COVID coughing and speech datasets suggest that our strategy outperforms other current methods as measured by different metrics, thereby providing new insights on automated COVID19 diagnosis on top of existing methods.
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Published In
May 2024
134 pages
ISBN:9798400717284
DOI:10.1145/3670085
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Published: 22 August 2024
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ICMAI 2024
ICMAI 2024: 2024 9th International Conference on Mathematics and Artificial Intelligence
May 10 - 12, 2024
Beijing, China
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