COVID-19 Diagnosis and Detection Based on Deep Learning Models

Research Article
Open access

COVID-19 Diagnosis and Detection Based on Deep Learning Models

Yuhan Li 1*
  • 1 Teensen Genesis School    
  • *corresponding author 250509790@qq.com
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230172
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

The COVID-19 pandemic has caused widespread illness and death since its emergence in 2019. This study examines various Artificial Intelligence (AI) techniques for diagnosing and predicting COVID-19. One such method is the Hust 19 model, which employs a hybrid learning architecture of CNN and DNN models. The model divides CT scans into three types that identify COVID-19-related imaging features and implements a deep learning framework based on the VGG16 architecture. Another group of researchers developed a deep learning-based COVID-19 diagnostic system using multi-class and multi-center data, segmenting lungs and identifying COVID-19 infection slices. They evaluated the model's accuracy using Receiver Operating Characteristic (ROC) curves. A third group developed a deep neural network based on DenseNet121, standardizing input CXR images through anatomical landmark detection and registration, and segmenting lung lesions to diagnose pneumonia. A final group developed a three-dimensional deep learning model called COVNet, which takes CT images as input, extracts features from each slice using the ResNet50 backbone, merges the maximum features obtained by the AI model, and generates classification predictions for the entire CT scan. They also proposed a multi-decoder split network to improve the model's accuracy and efficiency. Experimental results show that the Deep learning AI system model and COVNet model are relatively good, with average sensitivity and specificity. The remaining models, particularly Hust-19, show prominent specificity, but high specificity leads to low sensitivity, making the overall model imbalanced. These AI diagnostic models are just the beginning, and there may be more inventions and creations in the future.

Keywords:

CONVID-19 Diagnosis, deep learning, computer vision

Li,Y. (2023). COVID-19 Diagnosis and Detection Based on Deep Learning Models. Applied and Computational Engineering,8,744-751.
Export citation

References

[1]. World Health Organization 2022 14.9 million excess deaths associated with the COVID-19 pandemic in 2020 and 2021 https://www.who.int/news/item/05-05-2022-14.9-million-excess-deaths-were-associated-with-the-covid-19-pandemic-in-2020-and-2021.

[2]. Ning W Lei S Yang J et al. 2020 Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning Nature biomedical engineering 4(12): 1197-1207.

[3]. Jin C Chen W Cao Y et al. 2020 Development and evaluation of an artificial intelligence system for COVID-19 diagnosis Nature communication 11(1): 5088.

[4]. Wang G Liu X Shen J et al. 2021 A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images Nature biomedical engineering 5(6): 509-521.

[5]. Ortiz A Trivedi A Desbiens J et al. 2022 Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes Scientific reports 12(1): 1716.

[6]. Szolovits P Patil R S Schwartz W B 1988 Artificial intelligence in medical diagnosis Annals of internal medicine 108(1): 80-87.

[7]. Das S Biswas S Paul A et al. 2018 AI Doctor: An intelligent approach for medical diagnosis Industry Interactive Innovations in Science, Engineering and Technology: Proceedings of the International Conference I3SET 2016 Springer Singapore 173-183.

[8]. Yu Q Wang J Jin Z et al. 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control 72: 103323.

[9]. Lo S C B Chan H P Lin J S et al. 1995 Artificial convolution neural network for medical image pattern recognition Neural networks 8(7-8): 1201-1214.

[10]. Xie L Wisse L E M Wang J et al. 2023 Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation Medical Image Analysis, 83: 102683.

Cite this article

Li,Y. (2023). COVID-19 Diagnosis and Detection Based on Deep Learning Models. Applied and Computational Engineering,8,744-751.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. World Health Organization 2022 14.9 million excess deaths associated with the COVID-19 pandemic in 2020 and 2021 https://www.who.int/news/item/05-05-2022-14.9-million-excess-deaths-were-associated-with-the-covid-19-pandemic-in-2020-and-2021.

[2]. Ning W Lei S Yang J et al. 2020 Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning Nature biomedical engineering 4(12): 1197-1207.

[3]. Jin C Chen W Cao Y et al. 2020 Development and evaluation of an artificial intelligence system for COVID-19 diagnosis Nature communication 11(1): 5088.

[4]. Wang G Liu X Shen J et al. 2021 A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images Nature biomedical engineering 5(6): 509-521.

[5]. Ortiz A Trivedi A Desbiens J et al. 2022 Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes Scientific reports 12(1): 1716.

[6]. Szolovits P Patil R S Schwartz W B 1988 Artificial intelligence in medical diagnosis Annals of internal medicine 108(1): 80-87.

[7]. Das S Biswas S Paul A et al. 2018 AI Doctor: An intelligent approach for medical diagnosis Industry Interactive Innovations in Science, Engineering and Technology: Proceedings of the International Conference I3SET 2016 Springer Singapore 173-183.

[8]. Yu Q Wang J Jin Z et al. 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control 72: 103323.

[9]. Lo S C B Chan H P Lin J S et al. 1995 Artificial convolution neural network for medical image pattern recognition Neural networks 8(7-8): 1201-1214.

[10]. Xie L Wisse L E M Wang J et al. 2023 Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation Medical Image Analysis, 83: 102683.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.