Deep Learning Methods for Virus Identification from Digital Images
2020 35th International Conference on Image and Vision Computing New Zealand
; 2020.
Article
in English
| Web of Science | ID: covidwho-1349145
ABSTRACT
The use of deep learning methods for virus identification from digital images is a timely research topic. Given an electron microscopy image, virus recognition utilizing deep learning approaches is critical at present, because virus identification by human experts is relatively slow and time-consuming. In this project, our objective is to develop deep learning methods for automatic virus identification from digital images, there are four viral species taken into consideration, namely, SARS, MERS, HIV, and COVID-19. In this work, we firstly examine virus morphological characteristics and propose a novel loss function which aims at virus identification from the given electron micrographs. We take into account of attention mechanism for virus locating and classification from digital images. In order to generate the most reliable estimate of bounding boxes and classification for a virus as visual object, we train and test five deep learning models R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD, based on our dataset of virus electron microscopy. Additionally, we explicate the evaluation approaches. The conclusion reveals SSD and Faster R-CNN outperform in the virus identification.
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Collection:
Databases of international organizations
Database:
Web of Science
Language:
English
Journal:
2020 35th International Conference on Image and Vision Computing New Zealand
Year:
2020
Document Type:
Article
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