Version 1
: Received: 26 July 2022 / Approved: 27 July 2022 / Online: 27 July 2022 (10:01:54 CEST)
How to cite:
Zhu, Z. A Simple Convolutional Neural Network for Precise and Automated Identification of COVID-19. Preprints2022, 2022070419. https://doi.org/10.20944/preprints202207.0419.v1
Zhu, Z. A Simple Convolutional Neural Network for Precise and Automated Identification of COVID-19. Preprints 2022, 2022070419. https://doi.org/10.20944/preprints202207.0419.v1
Zhu, Z. A Simple Convolutional Neural Network for Precise and Automated Identification of COVID-19. Preprints2022, 2022070419. https://doi.org/10.20944/preprints202207.0419.v1
APA Style
Zhu, Z. (2022). A Simple Convolutional Neural Network for Precise and Automated Identification of COVID-19. Preprints. https://doi.org/10.20944/preprints202207.0419.v1
Chicago/Turabian Style
Zhu, Z. 2022 "A Simple Convolutional Neural Network for Precise and Automated Identification of COVID-19" Preprints. https://doi.org/10.20944/preprints202207.0419.v1
Abstract
To solve two key problems in the identification of people who are infected with COVID-19: the first problem is that the identification accuracy is not high enough. The second problem is that present identification method such as nucleic acid testing is expensive in many countries. Methods: So, I decided to design a fast identification method for COVID-19 patients which is based on deep learning. After the model (CoughNet) learns more than 6,000 cough spectrograms of both COVID-19 patients and normal people, the accuracy rate of identification of COVID-19 patients and normal people is higher than 99% in the test set. Structure: This paper is mainly divided into three parts: the first part introduces the main background and research status of the research; The second part introduces the research methods; The third part introduces the specific process of the experiment.
Keywords
computer vision; deep learning; CoughNet model
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.
