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Related Experiment Video

Updated: Aug 20, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

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Published on: December 19, 2020

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New patch-based strategy for COVID-19 automatic identification using chest x-ray images.

Jorge A Portal-Diaz1, Orlando Lovelle-Enríquez2, Marlen Perez-Diaz3

  • 1Informatic Office, Universidad Central Marta Abreu de Las Villas, Santa Clara, Cuba.

Health and Technology
|November 21, 2022
PubMed
Summary

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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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This summary is machine-generated.

A new method using segmented lung regions and patch partitioning on chest X-rays improves COVID-19 identification, overcoming shortcut learning issues. This approach enhances model generalization for reliable clinical use.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Developing accurate COVID-19 detection models from chest X-rays is crucial.
  • Existing models struggle with limited, low-quality datasets and shortcut learning, leading to unreliable performance.
  • Shortcut learning causes models to focus on irrelevant features instead of actual lung pathology.

Purpose of the Study:

  • To propose a novel image classification methodology to mitigate shortcut learning in COVID-19 detection.
  • To improve the reliability and generalization of artificial intelligence models for identifying COVID-19 from chest X-rays.
  • To address the challenge of data scarcity and quality in medical image analysis.

Main Methods:

  • Expert radiologist annotation of chest X-ray images.
Keywords:
Automatic classificationCOVID-19Chest X-Rays

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  • Segmentation of lung regions to focus analysis.
  • A novel classification strategy using patch partitioning to enhance convolutional neural network resolution.
  • Release of a native image dataset for external validation.

    • Main Results:

    • The 6-patch splitting variant achieved 0.887 accuracy, 0.85 recall, and 0.848 F1-score on the external validation set.
    • The proposed strategy demonstrated similar performance between internal and external validation.
    • This indicates strong generalization capabilities of the developed model.

    Conclusions:

    • The new strategy effectively mitigates shortcut learning in COVID-19 detection from chest X-rays.
    • The model exhibits robust generalization power, making it suitable for deployment in hospital settings.
    • The research contributes a validated methodology and a valuable external dataset for future studies.