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Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier
Open accessAbstract
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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“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]
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Retracted
Metadata
- 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)
- 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
- 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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