AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images

Rajkumar Soundrapandiyan; Himanshu Naidu; Marimuthu Karuppiah; M Maheswari; Ramesh Chandra Poonia

doi:10.1016/j.compeleceng.2023.108711

AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images

Comput Electr Eng. 2023 May:108:108711. doi: 10.1016/j.compeleceng.2023.108711. Epub 2023 Apr 11.

Authors

Rajkumar Soundrapandiyan¹, Himanshu Naidu², Marimuthu Karuppiah³, M Maheswari⁴, Ramesh Chandra Poonia⁵

Affiliations

¹ School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India.
² ServiceNow, Hyderabad, Telangana 500081, India.
³ School of Computer Science and Engineering & Information Science, Presidency University, Bengaluru, Karnataka 560064, India.
⁴ Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India.
⁵ Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka 560029, India.

Abstract

A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy.

Keywords: Accuracy; COVID-19; DarkNet19; Data augmentation; Deep learning; Discrete wavelet transform; ResNet50; VGG19; WavStaCovNet-19; X-ray images; Xception.