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On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays
Gabriel Iluebe Okolo1, Stamos Katsigiannis2, Turke Althobaiti3
1School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK.
Sensors (Basel, Switzerland)
|September 10, 2021
Summary
Deep learning models achieved high accuracy in diagnosing COVID-19 from chest X-rays. This AI approach offers a rapid and accessible tool for detecting coronavirus disease 2019 (COVID-19) with minimal resources.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology
Background:
- The COVID-19 pandemic highlighted the need for rapid, accessible diagnostic tools.
- Chest radiography presents a cost-effective imaging modality for disease detection.
Purpose of the Study:
- To evaluate deep convolutional neural network (CNN) architectures for classifying chest X-ray images.
- To assess the efficacy of modified CNNs in distinguishing between healthy individuals, COVID-19 cases, and viral pneumonia.
Main Methods:
- Eleven established CNN architectures were examined.
- Architectures were modified by adding extra layers to enhance performance.
- The models were trained and evaluated on a dataset of real chest X-ray images.
Main Results:
- The best-performing CNN model achieved a classification accuracy of 98.04%.
- The highest F1-score reached was 98.22%, indicating high precision and recall.
- Evaluated multiple deep learning approaches for COVID-19 detection.
Conclusions:
- Deep learning models, particularly modified CNNs, show significant promise for accurate COVID-19 diagnosis using chest X-rays.
- AI-powered analysis of radiographic images can support rapid and reliable detection of COVID-19.
- This approach offers a scalable solution for disease screening in resource-limited settings.
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