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Detection of COVID-19 From Chest X-Ray Images Using Machine Learning
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Author(s): Sushmita Pramanik Dutta (Bijoy Krishna Girls' College, India), Sriparna Saha (Maulana Abul Kalam Azad University of Technology, West Bengal, India)and Aniruddha Dey (Maulana Abul Kalam Azad University of Technology, West Bengal, India)
Copyright: 2023
Pages: 13
Source title:
Machine Learning and AI Techniques in Interactive Medical Image Analysis
Source Author(s)/Editor(s): Lipismita Panigrahi (GITAM University (Deemed), India), Sandeep Biswal (O.P. Jindal University, India), Akash Kumar Bhoi (KIET Group of Institutions, India & Sikkim Manipal University, India), Akhtar Kalam (Victoria University, Australia)and Paolo Barsocchi (Institute of Information Science and Technologies, Italy)
DOI: 10.4018/978-1-6684-4671-3.ch004
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Abstract
COVID-19 is a pandemic caused by novel coronavirus. Molecular diagnostic tests and serologic tests are the two types of testing procedures currently available to detect virus of COVID-19 in the human body. Another way to detect COVID-19 is the use of chest x-ray. Radiology doctors use x-ray report to identify COVID-19-positive patients and the severity of that. In the proposed methodology, the authors develop an algorithm that can detect COVID-19 from chest x-ray images automatically. In the proposed work, image descriptor such as local binary pattern (LBP) is used to extract the features of the x-ray images. Using those textural pattern, LBP image of the original image is generated, and a meta-data has been curated. This modified dataset is given as input to the convolutional neural network (CNN) for classification of the images. The CNN identifies whether the x-ray image is of a COVID-19-affected person or a healthy person with accuracy measured as high as 92.24%.
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