HOG and Haralick Feature Extractions with Machine Learning Methods for Covid-19 X-Ray Image Classification

HOG and Haralick Feature Extractions with Machine Learning Methods for Covid-19 X-Ray Image Classification
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© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : Nurul Nadiah Abd Rahman, Marina Yusoff, Murizah Kassim
DOI : 10.14445/22315381/IJETT-V70I10P202

How to Cite?

Nurul Nadiah Abd Rahman, Marina Yusoff, Murizah Kassim, "HOG and Haralick Feature Extractions with Machine Learning Methods for Covid-19 X-Ray Image Classification," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 8-17, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P202

Abstract
Distinguish COVID-19 from other respiratory diseases remains a demand mainly in machine learning solutions. The overlapping symptoms can confuse identifying the type of disease and lead to misdiagnosis. This paper evaluates feature extraction methods in conjunction with machine learning to determine a positive COVID-19 class. Pre-processing operations on a benchmark COVID-19 x-ray images dataset include under-sampling, resizing, converting into grayscale images, and noise removal. These operations are carried out to reduce the data produced by the dataset. A hybrid approach was used to conduct the evaluation, with Histogram of Oriented Gradient and Haralick as feature extractor methods and Support Vector Machine and K-Nearest Neighbors as classifiers. Several different parameters help measure the classifiers' performance. Compared to other hybrid methods, the Support Vector Machine with a Histogram of Oriented Gradient feature extraction performed the best. It has the highest accuracy score possible, coming in at 93.31%. The feature extraction method contributes to higher performance in the x-ray image classifier. In the future, additional feature extraction strategies, such as deep learning, may be potential competitors to this work.

Keywords
COVID-19, Feature Extraction, Machine Learning, Support Vector Machine, K-Nearest Neighbors.

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