Title: Amalgamation of wavelet transform and neural network for COVID-19 detection

Authors: Madhu Jain; Renu Sharma

Addresses: Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, India ' Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, India

Abstract: A zoonotic natured virus affecting almost every part of the globe is COVID-19. Early detection of such disease may lead to curable affairs. Since then, many research institutes have been trying to find effective methods for detecting and curing COVID-19. Real-time polymerase chain reaction test is also a method used for detection of the COVID-19. But, due to its accuracy rate and availability of kit, it is not relied on. Here, a combination of machine learning and wavelet transform based algorithm for chest X-ray classification is proposed. Image pre-processing is done using wavelet transform and further the classification is done using convolution neural network. It is a multi-class classifier, which will classify whether input image is COVID-19 affected, pneumonia or not affected. The dataset collected for this study from an open-source repository. It comprises 2,550 images of each class. For quantitative analysis of the proposed architecture, parameters such as accuracy, precision, F1 score, recall and sensitivity are measured.

Keywords: image classification; image enhancement; convolutional neural networks; CNN; X-rays; wavelet transforms; COVID-19.

DOI: 10.1504/IJBET.2024.136919

International Journal of Biomedical Engineering and Technology, 2024 Vol.44 No.2, pp.133 - 152

Received: 10 Oct 2022
Accepted: 18 Mar 2023
Published online: 29 Feb 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article