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Detection of covid 19 from an imbalanced chest x-ray image data set

Manjunath, Sharath Madhu orcid logoORCID: 0000-0002-1894-5331, Gurjar, Manju, O'Kane, Neil, McCarren, Andrew orcid logoORCID: 0000-0002-7297-0984 and Gualano, Leonardo (2022) Detection of covid 19 from an imbalanced chest x-ray image data set. In: 29th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2021), 9-10 Dec 2021, Dublin, Ireland (Online).

Abstract
The Covid-19 pandemic has spread quickly, making identification of the virus critically important in assisting overburdened healthcare systems. Numerous techniques have been used to identify Covid-19, of which the Polymerase chain reaction (PCR) test is the most common. However, obtaining results from the PCR test can take up to two days. An alternative is to use X-ray images of the subject’s chest area as inputs to a deep learning neural networks algorithm. The two problems with this approach are the choice of architecture and the method used to deal with the imbalanced data. In this study a comparative analysis of a standard convolutional neural network (CNN) and a number of transfer learning algorithms with a range of imbalanced data techniques was conducted to detect Covid-19 from a data set of chest x-ray images. This data set was an amalgamation of two data sets extracted from the Kaggle Covid-19 open source data repository and non-Covid illnesses taken from the National Institute of Health. The resulting data set was had over 115k records and 15 different type of findings ranging from no-illness to illnesses such as Covid-19, emphysema and lung cancer. This study addresses the problem of class imbalance on the largest data set used for x-ray detection of Covid-19 by combining undersampling and oversampling methods. The results showed that a CNN model in conjunction with these random over and under sampling methods outperformed all other candidates when identifying Covid-19 with a F1-score of 93%, a precision of 90% and a recall of 91%.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:No
Uncontrolled Keywords:Covid-19; Oversampling; Undersampling; CNN; transfer learning; chest x-ray
Subjects:Computer Science > Artificial intelligence
Computer Science > Image processing
Computer Science > Machine learning
Medical Sciences > Diseases
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: CEUR Workshop Proceedings. 3105. CEUR-WS.
Publisher:CEUR-WS
Official URL:http://ceur-ws.org/Vol-3105/
Copyright Information:© 2021 The Authors (CC-BY-4.0)
ID Code:26657
Deposited On:31 Jan 2022 16:16 by Leonardo Gualano . Last Modified 03 Oct 2022 13:18
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