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Related Concept Videos

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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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An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Related Experiment Video

Updated: Sep 4, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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An AI-enabled pre-trained model-based Covid detection model using chest X-ray images.

Rajeev Kumar Gupta1, Nilesh Kunhare2, Nikhlesh Pathik3

  • 1Pandit Deendayal Energy University, Gandhinagar, India.

Multimedia Tools and Applications
|July 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an AI approach using chest X-rays for rapid COVID-19 detection. The InceptionResNetV2 model achieved 97% test accuracy, aiding early COVID-19 diagnosis and control.

Keywords:
Convolution neural networkCovid-19InceptionResNetV2MobileNetV2Pre-trained modelResnet50VGG19

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • COVID-19 became a global pandemic in 2020-2021, causing widespread mortality.
  • Traditional COVID-19 testing methods are time-consuming, hindering rapid case identification.
  • Early detection is crucial for controlling COVID-19 transmission through isolation and timely treatment.

Purpose of the Study:

  • To develop an AI-based approach for preliminary COVID-19 screening using X-ray images.
  • To evaluate the performance of pre-trained deep learning models for COVID-19 detection.
  • To optimize a model for accurate classification of COVID-19 cases from normal cases.

Main Methods:

  • Utilized four pre-trained models: VGGNet-19, ResNet50, InceptionResNetV2, and MobileNet.
  • Trained models on chest X-ray images for binary classification (COVID-19 vs. Normal).
  • Employed normalization, regularization, and an updated binary cross-entropy loss function to penalize misclassification of COVID-19 cases.

Main Results:

  • The InceptionResNetV2 model demonstrated superior performance.
  • Achieved high accuracy rates: 99.2% (training), 98% (validation), and 97% (testing).
  • The model was tuned to minimize the misclassification of COVID-19 cases as normal.

Conclusions:

  • AI-based analysis of chest X-rays shows promise for rapid COVID-19 screening.
  • The InceptionResNetV2 model offers a robust solution for preliminary COVID-19 detection.
  • This approach can significantly aid in controlling the spread of COVID-19.