Virus Classification by Using a Fusion of Texture Analysis Methods

Transmission Electron Microscopy (TEM) is one of the most important tools for virus detection and identification. It enables researchers to study the characteristics of a virus in order to classify it into groups and families. From morphological characteristics of viruses in TEM images, this paper p...

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Bibliographic Details
Main Authors: Backes, Andre R., de Mesquita Sa Junior, Jarbas Joaci
Format: Conference Proceeding
Language:English
Subjects:
Computer vision
Discrete cosine transforms
Feature extraction
Microscopy
Support vector machines
Texture descriptors
Texture signature fusion
Virus classification
Viruses (medical)
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Summary:Transmission Electron Microscopy (TEM) is one of the most important tools for virus detection and identification. It enables researchers to study the characteristics of a virus in order to classify it into groups and families. From morphological characteristics of viruses in TEM images, this paper proposes a computer vision approach for virus classification. For this purpose, we compared ten texture descriptors methods and fused them in order to obtain a higher discriminative signature. We compared this signature to other works that addressed the same virus classification problem. The result achieved by our proposed fusion approach (87.27%) surpassed the highest accuracies of previous papers, thus proving to be very effective for discriminating virus in TEM images. Thus, we believe that our paper brings a relevant contribution to this important medical problem.
ISSN:2157-8702
DOI:10.1109/IWSSIP48289.2020.9145325