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Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
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Updated: Oct 20, 2025

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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
CNNCOVID-19chest X-raydeep learningimage classification

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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.