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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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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: Jun 1, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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An Efficient Dual-Sampling Approach for Chest CT Diagnosis.

Khalaf Alshamrani1,2, Hassan A Alshamrani1

  • 1Radiology Sciences Department, College of Medical Sciences, Najran University, Najran, Saudi Arabia.

Journal of Multidisciplinary Healthcare
|January 22, 2025
PubMed
Summary
This summary is machine-generated.

A new dual-sampling network improves lung abnormality detection in CT scans, outperforming uniform sampling. This AI advancement aids radiologists in accurate lung cancer diagnosis and patient care.

Keywords:
KNNSVMbalanced class samplingdual samplinglung cancerunder sampling and over fittinguniform sampling

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate diagnosis of lung abnormalities in computed tomography (CT) images is crucial for distinguishing cancerous from normal tissue.
  • The variable presentation of lung cancer necessitates advanced diagnostic approaches for precise identification.

Purpose of the Study:

  • To develop and evaluate a novel dual-sampling network for enhanced lung abnormality detection in CT images.
  • To address the challenge of unevenly distributed lung infection areas, which can be minor or major.

Main Methods:

  • Analysis of 150 CT images using a dual-sampling network.
  • Comparison of the proposed dual-sampling technique against a uniform sampling method.

Main Results:

  • The dual-sampling network achieved higher performance metrics: F1-score of 94.9%, accuracy of 95.2%, sensitivity of 94.2%, specificity of 96.1%, and AUC of 95.5%.
  • Uniform sampling yielded an F1-score of 94.2%, accuracy of 94.5%, sensitivity of 93.5%, specificity of 95.4%, and AUC of 98.4%.

Conclusions:

  • The dual-sampling network significantly enhances the precision of lung abnormality diagnosis in CT scans.
  • This AI-driven approach supports radiologists in achieving more accurate diagnoses, improving patient treatment outcomes and population health.