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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
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Correction: Diagnosis of COVID‑19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques.

SN computer science·2023
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Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques.

S Ezhil Mukhi1, R Thanuja Varshini1, S Eliza Femi Sherley1

  • 1Department of Information Technology, MIT, Anna University, Chromepet, Chennai, Tamil Nadu 600044 India.

SN Computer Science
|February 22, 2023
PubMed
Summary

This study shows that the VGG-19 deep learning model accurately detects COVID-19 using chest X-rays and CT scans. Chest X-rays proved more effective than CT scans for early COVID-19 detection.

Keywords:
COVID-19Deep learningMachine learningOptimization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Infectious Disease Diagnostics

Background:

  • The COVID-19 pandemic significantly impacted global health and healthcare systems.
  • Early detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is crucial for disease containment and public well-being.
  • Deep learning, particularly Convolutional Neural Networks (CNNs), shows promise in analyzing medical images for disease detection.

Purpose of the Study:

  • To develop and evaluate a deep learning classification method for detecting COVID-19 using chest X-ray and CT scan images.
  • To compare the performance of various CNN models for COVID-19 detection.
  • To determine the optimal model and imaging modality for accurate and efficient COVID-19 screening.

Main Methods:

  • Utilized deep learning models, including VGG-19, ResNet-50, Inception v3, and Xception, for image classification.
  • Trained and optimized CNN models using pre-processed chest X-ray and CT scan images from the Kaggle repository.
  • Evaluated and compared model performance based on detection accuracy.

Main Results:

  • The fine-tuned VGG-19 model achieved high accuracy in detecting COVID-19.
  • VGG-19 demonstrated 94.17% accuracy for chest X-rays and 93% accuracy for CT scans.
  • Chest X-rays yielded superior detection accuracy compared to CT scans when using the VGG-19 model.

Conclusions:

  • The VGG-19 model is highly suitable for detecting COVID-19 from medical images.
  • Chest X-rays are a more accurate and potentially cost-effective imaging modality for COVID-19 screening compared to CT scans.
  • Deep learning approaches offer a reliable method for rapid COVID-19 detection, aiding healthcare professionals and reducing exposure risk.