文章基本信息

标题：Improvement on KNN using genetic algorithm and combined feature extraction to identify COVID-19 sufferers based on CT scan imag
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作者：Radityo Adi Nugroho ; Arie Sapta Nugraha ; Aylwin Al Rasyid 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2021
卷号：19
期号：5
DOI：10.12928/telkomnika.v19i5.18535
语种：English
出版社：Universitas Ahmad Dahlan
摘要：Coronavirus disease 2019 (COVID-19) has spread throughout the world. The detection of this disease is usually carried out using the reverse transcriptase polymerase chain reaction (RT-PCR) swab test. However, limited resources became an obstacle to carrying out the massive test. To solve this problem, computerized tomography (CT) scan images are used as one of the solutions to detect the sufferer. This technique has been used by researchers but mostly using classifiers that required high resources, such as convolutional neural network (CNN). In this study, we proposed a way to classify the CT scan images by using the more efficient classifier, k-nearest neighbors (KNN), for images that are processed using a combination of these feature extraction methods, Haralick, histogram, and local binary pattern. Genetic algorithm is also used for feature selection. The results showed that the proposed method was able to improve KNN performance, with the best accuracy of 93.30% for the combination of Haralick and local binary pattern feature extraction, and the best area under the curve (AUC) for the combination of Haralick, histogram, and local binary pattern with a value of 0.948. The best accuracy of our models also outperforms CNN by a 4.3% margin.
关键词：genetic algorithm;Haralick;histogram;k-nearest neighbour;local binary pattern