Cited by 169COVID-19 diagnosis using AIAdvanced Neural Network ApplicationsRadiomics and Machine Learning in Medical Imaging

Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier

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Abraham, Bejoy, Nair, Madhu S.·2020·Biocybernetics and Biomedical Engineering

Abstract

Corona virus disease-2019 (COVID-19) is a pandemic caused by novel coronavirus. COVID-19 is spreading rapidly throughout the world. The gold standard for diagnosing COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR) test. However, the facility for RT-PCR test is limited, which causes early diagnosis of the disease difficult. Easily available modalities like X-ray can be used to detect specific symptoms associated with COVID-19. Pre-trained convolutional neural networks are widely used for computer-aided detection of diseases from smaller datasets. This paper investigates the effectiveness of multi-CNN, a combination of several pre-trained CNNs, for the automated detection of COVID-19 from X-ray images. The method uses a combination of features extracted from multi-CNN with correlation based feature selection (CFS) technique and Bayesnet classifier for the prediction of COVID-19. The method was tested using two public datasets and achieved promising results on both the datasets. In the first dataset consisting of 453 COVID-19 images and 497 non-COVID images, the method achieved an AUC of 0.963 and an accuracy of 91.16%. In the second dataset consisting of 71 COVID-19 images and 7 non-COVID images, the method achieved an AUC of 0.911 and an accuracy of 97.44%. The experiments performed in this study proved the effectiveness of pre-trained multi-CNN over single CNN in the detection of COVID-19.

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Crossref · 10.1016/j.bbe.2020.08.005

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  • CNN has become the primary tool for automatic COVID-19 diagnosis in many hospitals around the world using chest X-ray images.

    “CNN has become the primary tool for automatic COVID-19 diagnosis in many hospitals around the world using chest X-ray images [256–260].”

    From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [258]

    The Introduction and Related works sections describe CNN-based research on detecting COVID-19 from chest X-rays, and the Abstract reports tests of the authors’ method on public datasets. They do not establish that CNNs had become the primary diagnostic tool in hospitals worldwide; the Introduction instead calls RT-PCR the diagnostic gold standard. Page numbers are unavailable in our copy. (LLM, conf=0.87)

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Authors
Abraham, B., & Nair, M. S.
Venue
Biocybernetics and Biomedical Engineering
Publisher
Elsevier BV
Published
2020-10
Volume
40
Issue
4
Pages
1436-1445
ISSN
0208-5216 (ISSN-L)
OpenAlex
W3082245570
Cited by
169
References
48
Citations by year
2026: 1 · 2025: 6 · 2024: 14 · 2023: 40 · 2022: 48 · 2021: 57 · 2020: 3
FWCI
18.01
Type
journal article
Language
en
Open access
Locations
  • Journal of Applied Biomedicine (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
COVID-19 detection, chest X-ray images, computer-aided diagnosis, pre-trained convolutional neural networks, correlation-based feature selection, feature extraction, automated disease diagnosis

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