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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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CovMnet-Deep Learning Model for classifying Coronavirus (COVID-19).

Malathy Jawahar1, Jani Anbarasi L2, Vinayakumar Ravi3

  • 1Leather Process Technology Division, CSIR-Central Leather Research Institute, Adyar, Chennai 600020 India.

Health and Technology
|August 15, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning model, CovMnet, accurately classifies COVID-19 cases from normal chest X-rays with 97.4% accuracy. This AI tool aids radiologists in diagnosing lung disorders and monitoring COVID-19 non-invasively.

Keywords:
COVID-19Chest X-rayConvolutional neural networkDeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Chest X-rays are crucial for diagnosing lung disorders, including COVID-19.
  • Manual interpretation of X-rays by radiologists is time-consuming and prone to errors.
  • The COVID-19 pandemic necessitates efficient and accurate diagnostic tools.

Purpose of the Study:

  • To develop and evaluate a deep learning model for classifying COVID-19 and normal chest X-ray images.
  • To improve the accuracy and efficiency of COVID-19 diagnosis using medical imaging.
  • To provide an AI-powered tool to assist radiologists.

Main Methods:

  • Utilized deep convolution neural network (CNN) techniques for image analysis.
  • Performed deep feature extraction and hyperparameter tuning of CNN models.
  • Trained and evaluated four variants of the CNN model, including the proposed CovMnet.

Main Results:

  • The proposed CovMnet model achieved a classification accuracy of 97.4% for distinguishing COVID-19 from normal chest X-rays.
  • This accuracy surpasses previously reported results in similar studies.
  • The model demonstrated effectiveness in classifying lung abnormalities.

Conclusions:

  • The CovMnet model shows significant potential as an efficient, non-invasive diagnostic tool for COVID-19.
  • This AI-driven approach can aid radiologists in monitoring COVID-19 disease progression.
  • Deep learning offers a promising avenue for enhancing medical image analysis in pandemic scenarios.