COVID-19 detection using deep learning classifiers and contrast-enhanced canny edge detected x-ray images

• Main Authors: Tao, Stefanus Hwa Kieu, Abdullah Bade, Mohd Hanafi Ahmad Hijazi, Kolivand, Hoshang
• Format: Article
• Language: English; English
• Published: IEEE Computer Society 2021
• Subjects: RA643-645 Disease (Communicable and noninfectious) and public health
• Online Access: https://eprints.ums.edu.my/id/eprint/31998/1/COVID-19%20detection%20using%20deep%20learning%20classifiers%20and%20contrast-enhanced%20canny%20edge%20detected%20x-ray%20images_ABSTRACT.pdf; https://eprints.ums.edu.my/id/eprint/31998/3/COVID-19%20detection%20using%20deep%20learning%20classifiers%20and%20contrast-enhanced%20canny%20edge%20detected%20x-ray%20images.pdf; https://eprints.ums.edu.my/id/eprint/31998/; https://ieeexplore.ieee.org/document/9520213; https://doi.org/10.1109/MITP.2021.3052205
• Institution: Universiti Malaysia Sabah

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 article aims to demonstrate an ensemble deep learning approach that can differentiate COVID-19 and p...