Kieu, STH, Bade, A, Hijazi, MHA and Kolivand, H (2021) COVID-19 Detection Using Integration of Deep Learning Classifiers and Contrast-Enhanced Canny Edge Detected X-Ray Images. IT Professional, 23 (4). pp. 51-56. ISSN 1520-9202

Preview

Text COVID-19 Detection Using Integration of Deep Learning Classifiers and Contrast-Enhanced Canny Edge Detected X-Ray Images.pdf - Accepted Version Download (477kB) | Preview

Abstract

COVID-19 is a deadly disease, and should be efficiently detected. COVID-19 shares similar symptoms with pneumonia, another type of lung disease, which remains a cause of morbidity and mortality. This study aims to demonstrate an ensemble deep learning approach that can differentiate COVID-19 and pneumonia based on chest x-ray images. The original x-ray images were processed to produce two sets of images with different features. The first set was images enhanced with contrast limited adaptive histogram equalization. The second set was edge images produced by Contrast-Enhanced Canny Edge Detection. Convolutional neural networks were used to extract features from the images and train classifiers which were able to classify COVID-19, pneumonia and healthy lungs cases. Results show that the classifiers were able to differentiate x-rays of different classes, where the best performing ensemble achieved an overall accuracy of 97.90%, with a sensitivity of 99.47% and specificity of 98.94% for COVID-19 detection.

Item Type:	Article
Additional Information:	© 2021 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.
Uncontrolled Keywords:	0806 Information Systems
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions:	Computer Science & Mathematics
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited:	03 Sep 2021 10:40
Last Modified:	04 Sep 2021 05:05
DOI or ID number:	10.1109/mitp.2021.3052205
URI:	https://researchonline.ljmu.ac.uk/id/eprint/15439

View Item