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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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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net.

Aman Gupta1, Shashank Mishra1, Sourav Chandan Sahu1

  • 1Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur , Chhattisgarh India.

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|May 25, 2023
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Summary

This study introduces a novel method for rapid COVID-19 detection using chest X-rays and deep learning. The approach achieves high accuracy, offering a faster alternative to traditional RT-PCR testing for COVID-19 diagnosis.

Keywords:
COVID-19ClassificationClassifiesInceptionV3Lung segmentationTransfer learningX-ray images

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Diseases

Background:

  • The global spread of COVID-19 necessitates rapid diagnostic tools.
  • Current RT-PCR testing for COVID-19 is accurate but costly and time-consuming.
  • There is a need for innovative, efficient diagnostic methods to combat the pandemic.

Purpose of the Study:

  • To develop and evaluate a deep learning-based approach for COVID-19 detection using chest X-ray images.
  • To assess the efficacy of AI models in identifying COVID-19 indicators in radiographic data.
  • To provide a faster and potentially more accessible diagnostic alternative.

Main Methods:

  • Utilized chest X-ray images for COVID-19 detection.
  • Implemented pre-processing techniques including lung segmentation.
  • Employed deep learning models, specifically InceptionV3 and U-Net, with transfer learning.
  • Trained a Convolutional Neural Network (CNN) model for image classification.

Main Results:

  • Achieved high accuracy in classifying chest X-ray images as COVID-19 positive or negative.
  • The best performing models demonstrated approximately 99% detection accuracy.
  • The AI-driven analysis successfully identified patterns indicative of COVID-19.

Conclusions:

  • Deep learning models can effectively detect COVID-19 from chest X-ray images.
  • This AI-powered method shows promise as a rapid and accurate diagnostic tool.
  • The findings support the potential of medical imaging and AI in managing infectious disease outbreaks.