Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
Open accessAbstract
In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.
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Several convolutional neural network approaches for automatic detection from X-ray images in COVID-19 and pulmonary disease showed excellent performance.
“In the area of COVID-19 and pulmonary disease, several convolutional neural network approaches have been proposed for automatic detection from X-ray images which showed an excellent performance [46, 276–279].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [276]
With the help of artificial intelligence, X-ray images can be used to identify deep layer features which are not visible to the human eyes.
“However, with the help of artificial intelligence, the X-ray images can be used to identify the deep layer features which are not visible to the human eyes [73].”
From Can AI help in screening viral and COVID-19 pneumonia? (2020) · cites this paper as [73]
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- Authors
- Apostolopoulos, I. D., & Mpesiana, T. A.
- Affiliations
- University of Patras (GR)
- Corresponding author
- Apostolopoulos, Ioannis D.
- Venue
- Physical and Engineering Sciences in Medicine
- Published
- 2020-04-03
- Volume
- 43
- Issue
- 2
- Pages
- 635-640
- ISSN
- 2662-4729 (ISSN-L)
- 2662-4737
- PMC
- PMC7118364
- OpenAlex
- W3013601031
- Cited by
- 2,338
- References
- 18
- Citations by year
- 2026: 70 · 2025: 106 · 2024: 236 · 2023: 348 · 2022: 544 · 2021: 732 · 2020: 301 · 2012: 1
- FWCI
- 234.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physical and Engineering Sciences in Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- COVID-19 detection, chest X-ray images, transfer learning, convolutional neural networks, bacterial pneumonia, viral pneumonia, medical image classification
- MeSH terms
- Betacoronavirus, Deep Learning, COVID-19, SARS-CoV-2, Humans, Pneumonia, Viral, Radiographic Image Interpretation, Computer-Assisted, Radiography, Thoracic, Databases, Factual, Neural Networks, Computer, Coronavirus Infections, Pneumonia, Bacterial, Pandemics
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