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

• URL: http://arxiv.org/abs/2405.15563v1
• Date: Fri, 24 May 2024 13:52:14 GMT
• Title: Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images (Preprint)
• Authors: Niloy Sikder, Md. Al-Masrur Khan, Anupam Kumar Bairagi, Mehedi Masud, Jun Jiat Tiang, Abdullah-Al Nahid,
• Abstract summary: The analysis of Transmission Electron Microscopy (TEM) images has been proven to be quite successful in instant virus identification. This article proposes a deep learning-based classification model to identify the type of virus within those images correctly. 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.
• Score: 2.1346640951813165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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