Review
Version 2
Preserved in Portico This version is not peer-reviewed
Recent Machine Learning and Deep Learning Theories and Methods for COVID-19 Diagnosis
Version 1
: Received: 25 September 2023 / Approved: 26 September 2023 / Online: 26 September 2023 (10:36:55 CEST)
Version 2 : Received: 26 September 2023 / Approved: 27 September 2023 / Online: 27 September 2023 (10:29:04 CEST)
Version 2 : Received: 26 September 2023 / Approved: 27 September 2023 / Online: 27 September 2023 (10:29:04 CEST)
How to cite: Tang, W. Recent Machine Learning and Deep Learning Theories and Methods for COVID-19 Diagnosis. Preprints 2023, 2023091746. https://doi.org/10.20944/preprints202309.1746.v2 Tang, W. Recent Machine Learning and Deep Learning Theories and Methods for COVID-19 Diagnosis. Preprints 2023, 2023091746. https://doi.org/10.20944/preprints202309.1746.v2
Abstract
A long time has passed since COVID-19 was discovered and widely disseminated. Both machine learning and deep learning have moved towards the research of diagnosing COVID-19. Compared to deep learning, traditional machine learning-based methods can also achieve good diagnostic results if they are improved based on innovative points. And deep learning remains a more popular research object. Based on the deep neural network in the field of deep learning, the convolutional neural network, recurrent neural network, long and short-term memory network, and Transformer model have been extended. These models improve the performance and processing power of neural networks by introducing new structures and algorithms. This paper will introduce the basic concepts of machine learning and deep learning, as well as the details of using the relevant methods of both to help diagnose COVID-19.
Keywords
COVID-19; machine learning; deep learning; convolutional neural network
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Wenhao Tang
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