A Transfer Learning-Based Approach with Deep CNN for COVID-19- and Pneumonia-Affected Chest X-ray Image Classification

• Published in: SN computer science Vol. 3; no. 1; p. 17
• Main Authors: Chakraborty, Soarov, Paul, Shourav, Hasan, K. M. Azharul
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.01.2022; Springer Nature B.V
• Subjects: Accuracy; Big Data; Brain cancer; Breast cancer; Classification; Computer Aided Methods to Combat COVID-19 Pandemic; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Coronaviruses; COVID-19; Data Structures and Information Theory; Datasets; Deep learning; Disease; Image classification; Information Systems and Communication Service; Learning; Magnetic resonance imaging; Medical imaging; Original Research; Pattern Recognition and Graphics; Pneumonia; Software Engineering/Programming and Operating Systems; Tumors; Vision; Deep learning; COVID-19; Pneumonia; MongoDB; Transfer learning
• ISSN: 2662-995X; 2661-8907; 2661-8907
• DOI: 10.1007/s42979-021-00881-5

The COVID-19 pandemic creates a significant impact on everyone’s life. One of the fundamental movements to cope with this challenge is identifying the COVID-19-affected patients as early as possible. In this paper, we classified COVID-19, Pneumonia, and Healthy cases from the chest X-ray images by applying the transfer learning approach on the pre-trained VGG-19 architecture. We use MongoDB as a database to store the original image and corresponding category. The analysis is performed on a public dataset of 3797 X-ray images, among them COVID-19 affected (1184 images), Pneumonia affected (1294 images), and Healthy (1319 images) ( https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/version/3 ). This research gained an accuracy of 97.11%, average precision of 97%, and average Recall of 97% on the test dataset.