COVID-VIT: classification of Covid-19 from 3D CT chest images based on vision transformer model
Conference paper
Gao, X., Khan, M., Hui, R., Tian, Z., Qian, Y., Gao, A. and Baichoo, S. 2022. COVID-VIT: classification of Covid-19 from 3D CT chest images based on vision transformer model. 3rd International Conference on Next Generation Computing Applications (NextComp). Flic-en-Flac, Mauritius 06 - 08 Oct 2022 IEEE. https://doi.org/10.1109/NextComp55567.2022.9932246
Type | Conference paper |
---|---|
Title | COVID-VIT: classification of Covid-19 from 3D CT chest images based on vision transformer model |
Authors | Gao, X., Khan, M., Hui, R., Tian, Z., Qian, Y., Gao, A. and Baichoo, S. |
Abstract | This paper presents an explainable deep learning network to classify COVID from non-COVID based on 3D CT lung images. It applies a subset of the data for MIA-COV19 challenge through the development of 3D form of Vision Transformer deep learning architecture. The data comprise 1924 subjects with 851 being diagnosed with COVID, among them 1,552 being selected for training and 372 for testing. While most of the data volume are in axial view, there are a number of subjects’ data are in coronal or sagittal views with 1 or 2 slices are in axial view. Hence, while 3D data based classification is investigated, in this competition, 2D axial-view images remains the main focus. Two deep learning methods are studied, which are vision transformer (ViT) based on attention models and DenseNet that is built upon conventional convolutional neural network (CNN). Initial evaluation results indicates that ViT performs better than DenseNet with F1 scores being 0.81 and 0.72 respectively. (Codes are available at GitHub at https://github.com/xiaohong1/COVID-ViT). This paper illustrates that vision transformer performs the best in comparison to the other current state of the art approaches in classification of COVID from CT lung images. |
Sustainable Development Goals | 3 Good health and well-being |
Middlesex University Theme | Health & Wellbeing |
Conference | 3rd International Conference on Next Generation Computing Applications (NextComp) |
Proceedings Title | 2022 3rd International Conference on Next Generation Computing Applications (NextComp) |
ISBN | |
Electronic | 9781665469548 |
Electronic | 9781665469531 |
Paperback | 9781665469555 |
Publisher | IEEE |
Publication dates | |
06 Oct 2022 | |
Online | 31 Oct 2022 |
Publication process dates | |
Deposited | 30 Sep 2022 |
Accepted | 01 Jul 2022 |
Output status | Published |
Accepted author manuscript | File Access Level Open |
Copyright Statement | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Digital Object Identifier (DOI) | https://doi.org/10.1109/NextComp55567.2022.9932246 |
Scopus EID | 2-s2.0-85142375855 |
Related Output | |
Is new version of | COVID-VIT: classification of Covid-19 from CT chest images based on vision transformer models |
Language | English |
https://repository.mdx.ac.uk/item/8q053
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