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Aradhya, V.N.M., Mahmud, M. 
ORCID: 0000-0002-2037-8348, Guru, D.S., Agarwal, B. and Kaiser, M.S.,
2021.
One-shot cluster-based approach for the detection of COVID–19 from chest X–ray images.
Cognitive Computation.
ISSN 1866-9956
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1397299_a1774_Mahmud.pdf - Published version Download (851kB) | Preview |
Abstract
Coronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as of 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which
require further researching for various applications.
| Item Type: | Journal article | ||||||
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| Publication Title: | Cognitive Computation | ||||||
| Creators: | Aradhya, V.N.M., Mahmud, M., Guru, D.S., Agarwal, B. and Kaiser, M.S. | ||||||
| Publisher: | Springer | ||||||
| Date: | 2 March 2021 | ||||||
| ISSN: | 1866-9956 | ||||||
| Identifiers: |
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| Rights: | © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Jill Tomkinson | ||||||
| Date Added: | 12 Jan 2021 16:19 | ||||||
| Last Modified: | 26 Jul 2021 13:52 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/42024 |
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