Classification of COVID-19 and lung opacity using vision transformer on chest x-ray images
- There are several recent works which had proposed an automatic computer-aided diagnosis (CAD) deep learning (DL) model to diagnose coronavirus disease 2019 (COVID-19) using chest x-ray images (CXR) to propose a high-accuracy CAD method to detect COVID-19 automatically. In this study, seven different models including Convolutional Neural Network (CNN) models such as VGG-16 and vision transformer (ViT) models, are proposed. The different proposed models are trained with a three-class balanced dataset consisting of 3,000 CXR images consisting of 1,000 CXR images for each class of COVID-19, Normal, and Lung-Opacity. A publicly available dataset to train and test the models is used from Kaggle-COVID-19-Radiography-Dataset. From the experiments, the accuracy of the VGG16 model is 93.44% and ViT's is 92.33%. Besides, the binary classification between two classes of COVID-19 and Normal CXR with a limited number of just 100 images for each class, using a transfer learning technique, with a validation accuracy of 97.5% is proposed.
Document Type: | Article |
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Language: | English |
Author: | Manoochehr Noghanian Toroghi, Usman Ullah Sheikh, Shima Shahi Irani |
Parent Title (English): | Journal of Physics: Conference Series |
Volume: | 2622 |
Issue: | 1 |
Article Number: | 012016 |
Number of pages: | 7 |
ISSN: | 1742-6596 |
URN: | urn:nbn:de:hbz:1044-opus-77116 |
DOI: | https://doi.org/10.1088/1742-6596/2622/1/012016 |
Publisher: | IOP Publishing |
Publishing Institution: | Hochschule Bonn-Rhein-Sieg |
Date of first publication: | 2023/11/15 |
Keyword: | COVID-19; Convolutional Neural Network (CNN); VGG-16; Vision Transformer (ViT); transfer learning |
Dewey Decimal Classification (DDC): | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
Entry in this database: | 2024/01/18 |
Licence (German): | Creative Commons - CC BY - Namensnennung 3.0 |