An efficient method of detection of COVID-19 using Mask R-CNN on chest X-Ray images

Soumyajit Podder; Somnath Bhattacharjee; Arijit Roy; Soumyajit Podder; Somnath Bhattacharjee; Arijit Roy

doi:10.3934/biophy.2021022

AIMS Biophysics

2021, Volume 8, Issue 3: 281-290. doi: 10.3934/biophy.2021022

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Research article Special Issues

An efficient method of detection of COVID-19 using Mask R-CNN on chest X-Ray images

Department of Electronics, West Bengal State University, Barasat, Kolkata, India 700126

Received: 14 March 2021 Accepted: 02 July 2021 Published: 12 July 2021

Artificial intelligence techniques are used on chest X-ray images for accurate detection of diseases and this paper aims to develop a process which is capable of diagnosing COVID-19 using deep learning methods on X-ray images. For this purpose, we used Mask R-CNN method to train and test on the dataset to classify between patients infected and non-infected with COVID-19. The dataset used here contains a large number of frontal views of X-ray images which are an essential resource for the algorithms used in the development of tools for the detection of COVID-19. Using 668 chest X-ray images, the proposed model achieved an accuracy as high as 96.98%, specificity of 97.36% with the precision of 96.60%. The entire process is presented in detail. When a comparison table on the AI-based techniques is prepared, it is noticed that the Mask R-CNN technique on chest X-ray images provides better efficiency in the detection of COVID-19. The Mask R-CNN method is found to be accurate and robust in the detection of COVID-19 from chest X-ray images.
- COVID-19,
- chest radiography,
- deep learning,
- mask R-CNN,
- X-ray
Citation: Soumyajit Podder, Somnath Bhattacharjee, Arijit Roy. An efficient method of detection of COVID-19 using Mask R-CNN on chest X-Ray images[J]. AIMS Biophysics, 2021, 8(3): 281-290. doi: 10.3934/biophy.2021022

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Abstract

Artificial intelligence techniques are used on chest X-ray images for accurate detection of diseases and this paper aims to develop a process which is capable of diagnosing COVID-19 using deep learning methods on X-ray images. For this purpose, we used Mask R-CNN method to train and test on the dataset to classify between patients infected and non-infected with COVID-19. The dataset used here contains a large number of frontal views of X-ray images which are an essential resource for the algorithms used in the development of tools for the detection of COVID-19. Using 668 chest X-ray images, the proposed model achieved an accuracy as high as 96.98%, specificity of 97.36% with the precision of 96.60%. The entire process is presented in detail. When a comparison table on the AI-based techniques is prepared, it is noticed that the Mask R-CNN technique on chest X-ray images provides better efficiency in the detection of COVID-19. The Mask R-CNN method is found to be accurate and robust in the detection of COVID-19 from chest X-ray images.

Conflict of interest

The authors declare no conflicts of interest.

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