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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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Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Related Experiment Video

Updated: Aug 7, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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CoviExpert: COVID-19 detection from chest X-ray using CNN.

A Arivoli1, Devdatt Golwala1, Rayirth Reddy1

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.

Measurement. Sensors
|March 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces CoviExpert, a deep learning tool that accurately detects COVID-19 from Chest X-rays. This fast and inexpensive method aids medical professionals in diagnosing COVID-19, reducing reliance on radiation-heavy CT scans.

Keywords:
CNNCOVID-19CT scanCoviExpertDeep learningX-ray

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Radiology and Diagnostic Tools
  • Infectious Disease Detection

Background:

  • COVID-19 pandemic presents significant challenges to healthcare systems globally.
  • Increased demand for rapid and accessible diagnostic tools for COVID-19.
  • Concerns regarding high radiation exposure and cost associated with CT scans for COVID-19 diagnosis.

Purpose of the Study:

  • To develop an efficient and cost-effective method for detecting COVID-19 using Chest X-rays.
  • To leverage deep learning for automated COVID-19 diagnosis.
  • To provide a user-friendly tool for medical professionals to aid in rapid patient screening.

Main Methods:

  • Development of a deep learning model using a Convolutional Neural Network (CNN) with Keras.
  • Training the CNN model on a dataset of 1584 Chest X-ray images (COVID-19 positive and negative).
  • Integration of the trained model into a software application named CoviExpert with a front-end user interface.

Main Results:

  • The CoviExpert system achieved a high classification accuracy of 99% in detecting COVID-19 from Chest X-ray images.
  • The model demonstrated the ability to differentiate between COVID-19 positive and negative cases effectively.
  • The software provides rapid detection results within seconds.

Conclusions:

  • Deep learning applied to Chest X-rays offers a promising, accurate, and efficient approach for COVID-19 detection.
  • CoviExpert provides a valuable, low-radiation, and cost-effective alternative to CT scans for COVID-19 diagnosis.
  • The developed tool can assist healthcare professionals in timely diagnosis and patient management during the pandemic.