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

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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

DOI 10.1016/j.bbe.2020.08.005PMC PMC7467028

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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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Authors: Abraham, B., & Nair, M. S.
ORCID: Abraham, Bejoy
Nair, Madhu S.
Venue: Biocybernetics and Biomedical Engineering
Publisher: Elsevier BV
Published: 2020-10
Volume: 40
Issue: 4
Pages: 1436-1445
ISSN: 0208-5216 (ISSN-L)
DOI: 10.1016/j.bbe.2020.08.005
PMC: PMC7467028
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: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier

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