Title: Machine learning-based classification: an analysis based on COVID-19 transmission electron microscopy images

Authors: Kalyan Kumar Jena; Sourav Kumar Bhoi; Soumya Ranjan Nayak; Chinmaya Ranjan Pattanaik

Addresses: Department of Computer Science and Engineering, Parala Maharaja Engineering College, Berhampur, Odisha, India ' Department of Computer Science and Engineering, Parala Maharaja Engineering College, Berhampur, Odisha, India ' Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India ' Department of Computer Science and Engineering, Ajay Binay Institute of Technology, Cuttack, Odisha, India

Abstract: Virus is a type of microorganism which provides adverse effect on the human society. Viruses replicate within the human cells quickly. Currently, the effects of very dangerous infectious viruses are a major issue throughout the globe. Coronavirus (CV) is a very dangerous infectious virus which has adverse effects for the entire world. The Coronavirus Disease 2019 (COVID-19) infected cases are increasing day by day in a rapid manner. So, it is very important to detect and classify this type of virus at the initial stage so that preventive measures can be taken as early as possible. In this work, a Machine Learning (ML) based approach is focused for the type classification of Transmission Electron Microscopy (TEM) CV images (CVIs) such as alpha CV (ACV), beta CV (BCV) and gamma CV (GCV). The ML-based approach mainly focuses on several classification techniques such as Support Vector Machine (SVM), Random Forest (RF), AdaBoost (AB) and Decision Tree (DT) techniques for the processing of TEM CVIs. The performance of these techniques is analysed using the performance metrics such as Classification Accuracy (CA), Area Under receiver operating characteristic Curve (AUC), F1, Precision and Recall. The simulation of this work is carried out using Orange-3.26.0.

Keywords: COVID-19; machine learning; TEM CVIs; support vector machine; random forest; AdaBoost; decision tree; classification accuracy; AUC; microscopy images.

DOI: 10.1504/IJCAT.2021.120462

International Journal of Computer Applications in Technology, 2021 Vol.66 No.3/4, pp.350 - 361

Received: 06 Aug 2020
Accepted: 16 Sep 2020
Published online: 21 Jan 2022 *

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