COVID-opt-aiNet: A clinical decision support system for COVID-19 detection

Summrina Kanwal; Faiza Khan; Sultan Alamri; Kia Dashtipur; Mandar Gogate

doi:10.1002/ima.22695

COVID-opt-aiNet: A clinical decision support system for COVID-19 detection

Int J Imaging Syst Technol. 2022 Mar;32(2):444-461. doi: 10.1002/ima.22695. Epub 2022 Jan 3.

Authors

Summrina Kanwal¹, Faiza Khan², Sultan Alamri¹, Kia Dashtipur³, Mandar Gogate⁴

Affiliations

¹ Department of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia.
² Faculty of Computing Riphah International University Islamabad Pakistan.
³ James Watt School of Engineering University of Glasgow Glasgow UK.
⁴ School of Computing, Merchiston Campus, Edinburgh Napier University Edinburgh UK.

Abstract

Coronavirus disease (COVID-19) has had a major and sometimes lethal effect on global public health. COVID-19 detection is a difficult task that necessitates the use of intelligent diagnosis algorithms. Numerous studies have suggested the use of artificial intelligence (AI) and machine learning (ML) techniques to detect COVID-19 infection in patients through chest X-ray image analysis. The use of medical imaging with different modalities for COVID-19 detection has become an important means of containing the spread of this disease. However, medical images are not sufficiently adequate for routine clinical use; there is, therefore, an increasing need for AI to be applied to improve the diagnostic performance of medical image analysis. Regrettably, due to the evolving nature of the COVID-19 global epidemic, the systematic collection of a large data set for deep neural network (DNN)/ML training is problematic. Inspired by these studies, and to aid in the medical diagnosis and control of this contagious disease, we suggest a novel approach that ensembles the feature selection capability of the optimized artificial immune networks (opt-aiNet) algorithm with deep learning (DL) and ML techniques for better prediction of the disease. In this article, we experimented with a DNN, a convolutional neural network (CNN), bidirectional long-short-term memory, a support vector machine (SVM), and logistic regression for the effective detection of COVID-19 in patients. We illustrate the effectiveness of this proposed technique by using COVID-19 image datasets with a variety of modalities. An empirical study using the COVID-19 image dataset demonstrates that the proposed hybrid approaches, named COVID-opt-aiNet, improve classification accuracy by up to 98%-99% for SVM, 96%-97% for DNN, and 70.85%-71% for CNN, to name a few examples. Furthermore, statistical analysis ensures the validity of our proposed algorithms. The source code can be downloaded from Github: https://github.com/faizakhan1925/COVID-opt-aiNet.

Keywords: COVID‐19; bidirectional long‐short‐term memory; clinical decision support system; convolution neural network; deep learning neural network; feature selection; optimized artificial immune network; support vector machine.