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Machine Learning Approach for Autonomous Detection and Classification of COVID-19 Virus.

Osama R Shahin1, Hamoud H Alshammari2, Ahmed I Taloba1

  • 1Department of Computer Science, College of Science and Arts in Gurayat, Jouf University, SaudiArabia.

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|May 4, 2022
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Summary

This study introduces a machine learning approach for COVID-19 detection using CT scans and clinical data. The system aids doctors in accurate diagnosis, offering a potentially faster and more cost-effective alternative to RT-PCR tests.

Keywords:
CAD systemClinical specimens,SVMCovid-19 analysisRadial basis function

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Computational Biology

Background:

  • The global vulnerability to COVID-19 necessitates efficient diagnostic tools.
  • Computed Tomography (CT) lung screening is emerging as a potentially more accurate and cost-effective method for COVID-19 diagnosis compared to RT-PCR.
  • Clinical specimens provide crucial data for disease detection.

Purpose of the Study:

  • To develop and evaluate a machine learning-based Computer-Aided Diagnosis (CAD) system for detecting and classifying COVID-19.
  • To integrate CT lung screening data with clinical specimen analysis for enhanced diagnostic accuracy.
  • To provide a user-friendly graphical user interface (GUI) application for medical professionals.

Main Methods:

  • Utilized machine learning algorithms including Decision Tree, Support Vector Machine (SVM), K-means clustering, and Radial Basis Function (RBF).
  • Implemented a four-phase CAD system: CT lung screening, pre-processing for ground glass opacities (GGOs), modified K-means for segmentation, and SVM/RBF classification.
  • Incorporated 15 factors derived from serum, respiratory secretions, and whole blood specimens.

Main Results:

  • The proposed CAD system effectively detects and classifies COVID-19 using CT images and clinical data.
  • A modified K-means algorithm successfully segmented regions of interest, including GGOs.
  • The developed GUI application aids clinicians by presenting 15 input factors for clearer diagnostic outcomes.

Conclusions:

  • Machine learning techniques, particularly SVM and RBF, show promise in the automated detection and classification of COVID-19 from CT scans and clinical data.
  • The CAD system offers a valuable tool to assist healthcare professionals in diagnosing COVID-19, potentially improving efficiency and accuracy.
  • Integrating imaging and clinical data within a user-friendly interface enhances diagnostic decision-making for COVID-19.