Machine Learning‐Based Virus Type Classification Using Transmission Electron Microscopy Virus Images

Summary

Viruses are the submicroscopic infectious agents having the capability of replication itself inside the living cells of human body. Different dangerous infectious viruses greatly affect the human society along with plants, animals and microorganisms. It is very difficult for the survival of human society due to these viruses. In this chapter, Machine Learning (ML)‐based approach is used to analyze several transmission electron microscopy virus images (TEMVIs). In this work, several TEMVIs such as Ebola virus (EV), Entero virus (ENV), Lassa virus (LV), severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), Zika virus (ZV), etc. are analyzed. The ML‐based approach mainly focuses on the classification techniques such as Logistic Regression (LR), Neural Network (NN), k‐Nearest Neighbors (kNN) and Naive Bayes (NB) for the processing of TEMVIs. The performance of these techniques is analyzed using classification accuracy (CA) parameter. The simulation of this work is carried out using Orange3‐3.24.1.