Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images. (19th May 2021)

• Record Type: Journal Article
• Authors: Hasan, M. D. Kamrul; Ahmed, Sakil; Abdullah, Z. M. Ekram; Monirujjaman Khan, Mohammad; Anand, Divya; Singh, Aman; AlZain, Mohammad; Masud, Mehedi
• Other Names: Sun Kehui Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-05-19
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/9929274
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : The COVID-19 pandemic has wreaked havoc in the daily life of human beings and devastated many economies worldwide, claiming millions of lives so far. Studies on COVID-19 have shown that older adults and people with a history of various medical issues, specifically prior cases of pneumonia, are at a higher risk of developing severe complications from COVID-19. As pneumonia is a common type of infection that spreads in the lungs, doctors usually perform chest X-ray to identify the infected regions of the lungs. In this study, machine learning tools such as LabelBinarizer are used to perform one-hot encoding on the labeled chest X-ray images and transform them into categorical form using Python's to_categorical tool. Subsequently, various deep learning features such as convolutional neural network (CNN), VGG16, AveragePooling2D, dropout, flatten, dense, and input are used to build a detection model. Adam is used as an optimizer, which can be further applied to predict pneumonia in COVID-19 patients. The model predicted pneumonia with an average accuracy of 91.69%, sensitivity of 95.92%, and specificity of 100%. The model also efficiently reduces training loss and increases accuracy.