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MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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C-COVIDNet: A CNN Model for COVID-19 Detection Using Image Processing.

Neha Rajawat1, Bharat Singh Hada2, Mayank Meghawat2

  • 1Department of Mathematics, Career Point University, Kota, India.

Arabian Journal for Science and Engineering
|May 9, 2022
PubMed
Summary

A new deep learning model, C-COVIDNet, accurately detects COVID-19 from chest X-rays. This cost-effective method achieves 97.5% accuracy, aiding in rapid diagnosis and reducing false cases.

Keywords:
COVID-19 detectionConvolution neural networkDeep learningImage processing

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • The COVID-19 pandemic necessitates efficient and cost-effective diagnostic tools.
  • Chest X-ray analysis is a promising avenue for diagnosing COVID-19 due to its respiratory nature.
  • Existing diagnostic methods require improvement in speed and accuracy.

Purpose of the Study:

  • To introduce C-COVIDNet, an image processing-based model for COVID-19 detection.
  • To develop a lightweight Convolutional Neural Network (CNN) for analyzing chest X-rays.
  • To achieve high accuracy and efficiency in differentiating COVID-19 from Pneumonia and normal cases.

Main Methods:

  • Utilized a dataset of chest X-ray images categorized into COVID-19, Pneumonia, and Normal.
  • Implemented an image preprocessing pipeline to extract the Region of Interest (ROI).
  • Trained a lightweight CNN model (C-COVIDNet) using custom data generators for batch image processing.

Main Results:

  • C-COVIDNet achieved a diagnostic accuracy of 97.5%.
  • The model demonstrated a high F1-score of 97.91%.
  • Performance surpassed existing state-of-the-art methods in COVID-19 detection.

Conclusions:

  • C-COVIDNet shows significant potential as a deep learning-based diagnostic tool for COVID-19.
  • The model's high accuracy and efficiency support its use in clinical settings.
  • This research can accelerate the development of advanced radiography-based diagnostic solutions.