SVM-kNN- IPSO ensemble method for Diagnosis of Novel Coronavirus (COVID-19) with CT images

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Wial Abbas Hanon, et. al.

Abstract

New coronavirus epidemic- COVID- 19 is still growing. This epidemic disease not only includes high mortality due to viral infection but also caused the psychological disaster in all parts of the world. The paper provides the early Coronavirus stage detection COVID-19, with the methods of machine learning. Support vector machine (SVM) is a two-class classifier which in the recent years attracted a significant attention. The performance of this classifier depends on the amount of its parameters such as C (Penalty Factor) and the existing parameter in kernel. Also the selection of a suitable kernel function has a significant affect in its performance improvement. Besides the mentioned cases, performing the feature selection process not only causes to improve the mentioned performance improvement but also causes to reduce the computation complexity and training time. In this paper, we used the improved partial swarm optimization algorithm (IPSO) to optimize the SVM. Findings illustrated that proposed method could be utilized for diagnosing disease of COVID-19 as the assistant system. Promisingly, the proposed method can be regarded as a useful clinical decision tool for the physicians.

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