Compare Some Classification Methods for COVID-19 Identification with Application

Authors

  • Rusul Mustafa Ismaiel Technical College of management - Baghdad, Middle Technical University, Baghdad, Iraq.
  • Waleed Abdullah Araheemah Technical College of management - Baghdad, Middle Technical University, Baghdad, Iraq.

DOI:

https://doi.org/10.51173/jt.v4i4.614

Keywords:

Medical Image, COVID-19 Detection, BRISK, HARRIS, Imaging Features, Probability Density Function

Abstract

The continuous increase in new cases of COVID-19 worldwide and the potential for disease outbreaks require new tools to assist health professionals in early diagnosis and monitoring of patients. Suffer from sources of noise that you were exposed to during filming or treatment. This paper presents a technique for diagnosing the (Covid-19) virus through deep learning and the use of classification techniques and its use to treat and try to identify the infection or not. Where 2000 photos of infected and non-infected COVID-19 were taken and the (BRISK، HARRIS) Probability density function (PDF) method was applied for the purpose of extracting image features، finding and preserving image qualities pure to extract image features، find and preservextract the affected from the unaffected، as well as exclude other relevant influences، and the results can be compared using other methods such as large binary models and hybrid models.

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Published

2022-12-31

How to Cite

Rusul Mustafa Ismaiel, & Waleed Abdullah Araheemah. (2022). Compare Some Classification Methods for COVID-19 Identification with Application. Journal of Techniques, 4(4), 228–236. https://doi.org/10.51173/jt.v4i4.614

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