Multiclass Classification for Detection of COVID-19 Infection in Chest X-Rays Using CNN. (11th August 2022)

• Record Type: Journal Article
• Authors: Alharbi, Rawan Saqer; Alsaadi, Hadeel Aysan; Manimurugan, S.; Anitha, T.; Dejene, Minilu
• Other Names: Bhardwaj Arpit Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-08-11
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/3289809
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Coronavirus took the world by surprise and caused a lot of trouble in all the important fields in life. The complexity of dealing with coronavirus lies in the fact that it is highly infectious and is a novel virus which is hard to detect with exact precision. The typical detection method for COVID-19 infection is the RT-PCR but it is a rather expensive method which is also invasive and has a high margin of error. Radiographies are a good alternative for COVID-19 detection given the experience of the radiologist and his learning capabilities. To make an accurate detection from chest X-Rays, deep learning technologies can be involved to analyze the radiographs, learn distinctive patterns of coronavirus' presence, find these patterns in the tested radiograph, and determine whether the sample is actually COVID-19 positive or negative. In this study, we propose a model based on deep learning technology using Convolutional Neural Networks and training it on a dataset containing a total of over 35, 000 chest X-Ray images, nearly 16, 000 for COVID-19 positive images, 15, 000 for normal images, and 5, 000 for pneumonia-positive images. The model's performance was assessed in terms of accuracy, precision, recall, and F 1-score, and it achieved 99% accuracy, 0.98 precision, 1.02 recall, and 99.0% F 1-score, thus outperforming other deep learning models from other studies.