An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19. (7th June 2021)

• Record Type: Journal Article
• Authors: Serena Low, Woan Ching; Chuah, Joon Huang; Tee, Clarence Augustine T. H.; Anis, Shazia; Shoaib, Muhammad Ali; Faisal, Amir; Khalil, Azira; Lai, Khin Wee
• Other Names: Jolfaei Alireza Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2021(2021)
• Publication Date: 2021-06-07
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2021/5528144
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.