Compare Some Classification Methods for COVID-19 Identification with Application
DOI:
https://doi.org/10.51173/jt.v4i4.614Keywords:
Medical Image, COVID-19 Detection, BRISK, HARRIS, Imaging Features, Probability Density FunctionAbstract
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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Copyright (c) 2022 Rusul Mustafa Ismaiel, Waleed Abdullah Araheemah
This work is licensed under a Creative Commons Attribution 4.0 International License.
