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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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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Updated: Oct 2, 2025

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Detection of COVID-19 Based on Chest X-rays Using Deep Learning.

Walaa Gouda1, Maram Almurafeh2, Mamoona Humayun2

  • 1Department of Computer Engineering and Network, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi Arabia.

Healthcare (Basel, Switzerland)
|February 25, 2022
PubMed
Summary

Deep learning (DL) models using ResNet-50 on chest X-ray (CXR) images offer a highly accurate method for detecting coronavirus disease (COVID-19). This approach significantly aids in early diagnosis and disease containment efforts.

Keywords:
COVID-19chest X-raydeep transfer learningneural network (NN)pneumonia

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • The rapid global spread of coronavirus disease (COVID-19) necessitates efficient diagnostic tools.
  • Early identification and isolation of infected individuals are critical for disease control.
  • Deep learning (DL) presents a promising avenue for reliable and accessible COVID-19 detection.

Purpose of the Study:

  • To develop and evaluate deep learning models for COVID-19 detection using chest X-ray (CXR) images.
  • To compare the performance of proposed DL methods against existing techniques.
  • To establish a robust system for automated COVID-19 diagnosis from radiological data.

Main Methods:

  • Utilized two deep learning approaches based on the ResNet-50 architecture.
  • Implemented a preprocessing pipeline including augmentation, enhancement, normalization, and resizing of CXR images.
  • Employed an ensemble method with multiple runs of a modified ResNet-50 for image classification.

Main Results:

  • Achieved high performance metrics, including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC), exceeding 99.63% in many cases.
  • Demonstrated superior performance compared to established methods like VGG and Densnet.
  • Validated the proposed system on two public benchmark datasets: COVID-19 Image Data Collection (IDC) and CXR Images (Pneumonia).

Conclusions:

  • The proposed deep learning system, leveraging ResNet-50, demonstrates exceptional efficacy in detecting COVID-19 from CXR images.
  • This advanced AI approach offers a reliable and efficient tool for augmenting diagnostic capabilities in clinical settings.
  • The findings support the integration of DL-based solutions for rapid and accurate COVID-19 screening.