Diagnosing covid-19 lung inflammation using machine learning algorithms: A comparative study

被引：0

作者：

Ali A.M. ^{[1
]}

Ghafoor K.Z. ^{[1
,2
]}

Maghdid H.S. ^{[3
]}

Mulahuwaish A. ^{[4
]}

机构：

[1] Department of Software Engineering, Salahaddin University-Erbil, Erbil

[2] School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton

[3] Department of Software Engineering, Faculty of Engineering, Koya University, Kurdistan Region, Koysinjaq

[4] Department of Computer Science and Information Systems Saginaw, Valley State University, 7400 Bay Rd, Science East 174 University Center, Itta Bena, 48710, MI

来源：

Studies in Big Data | 2020年 / 80卷

关键词：

COVID-19; Level of COVID-19 severity; Machine learning algorithms; Pneumonia;

D O I：

10.1007/978-981-15-8097-0_4

中图分类号：

学科分类号：

摘要：

In this paper, we performed a comparative analysis using machine learning algorithms named support vector machine (SVM), decision tree (DT), k-nearest neighbor (kNN), and convolution neural network (CNN) to classify pneumonia level (mild, progressive, and severe stage) of the COVID-19 confirmed patients. More precisely, the proposed model consists of two phases: first, the model computes the volume and density of lesions and opacities of the CT images using morphological approaches. In the second phase, we use machine learning algorithms to classify the pneumonia level of the confirmed COVID-19 patient. Extensive experiments have been carried out and the results show the accuracy of 91.304%, 91.4%, 87.5%, 95.622% for kNN, SVM, DT, and CNN, respectively. © Springer International Publishing AG 2018.

引用

页码：91 / 105

页数：14

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