Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images

Abstract

Viruses are submicroscopic agents that can infect other lifeforms and use their hosts’ cells to replicate

themselves. Despite having simplistic genetic structures among all living beings, viruses are highly

adaptable, resilient, and capable of causing severe complications in their hosts’ bodies. Due to their

multiple transmission pathways, high contagion rate, and lethality, viruses pose the biggest biological

threat both animal and plant species face. It is often challenging to promptly detect a virus in a host

and accurately determine its type using manual examination techniques. However, computer-based

automatic diagnosis methods, especially the ones using Transmission Electron Microscopy (TEM)

images, have proven effective in instant virus identification. Using TEM images collected from a

recent dataset, this article proposes a deep learning-based classification model to identify the virus

type within those images. The methodology of this study includes two coherent image processing

techniques to reduce the noise present in raw microscopy images and a functional Convolutional

Neural Network (CNN) model for classification. Experimental results show that it can differentiate

among 14 types of viruses with a maximum of 97.44% classification accuracy and F1-score, which

asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart

a fast and dependable virus identification scheme subsidiary to the thorough diagnostic procedures.