  Viruses are submicroscopic agents that can infect all kinds of lifeforms and
use their hosts' living cells to replicate themselves. Despite having some of
the simplest genetic structures among all living beings, viruses are highly
adaptable, resilient, and given the right conditions, are capable of causing
unforeseen complications in their hosts' bodies. Due to their multiple
transmission pathways, high contagion rate, and lethality, viruses are the
biggest biological threat faced by animal and plant species. It is often
challenging to promptly detect the presence of a virus in a possible host's
body and accurately determine its type using manual examination techniques;
however, it can be done using computer-based automatic diagnosis methods. Most
notably, the analysis of Transmission Electron Microscopy (TEM) images has been
proven to be quite successful in instant virus identification. Using TEM images
collected from a recently published dataset, this article proposes a deep
learning-based classification model to identify the type of virus within those
images correctly. The methodology of this study includes two coherent image
processing techniques to reduce the noise present in the raw microscopy images.
Experimental results show that it can differentiate among the 14 types of
viruses present in the dataset 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 way of virus
identification subsidiary to the thorough diagnostic procedures.
