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

Kanwal, S., Khan, F., Alamri, S., Dashtipur, K. and Gogate, M. (2022) COVID-opt-aiNet: a clinical decision support system for COVID-19 detection. International Journal of Imaging Systems and Technology, 32(2), pp. 444-461. (doi: 10.1002/ima.22695) (PMID:35465215) (PMCID:PMC9015255)

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.

• Item Type: Articles
• Keywords: Bidirectional long-short-term memory, clinical decision support system, convolution neural network, COVID-1,9 deep learning neural network, feature selection, optimized artificial immune network, support vector machine.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Dashtipour, Dr Kia
• Authors: Kanwal, S., Khan, F., Alamri, S., Dashtipur, K., and Gogate, M.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: International Journal of Imaging Systems and Technology
• Publisher: Wiley
• ISSN: 0899-9457
• ISSN (Online): 1098-1098
• Published Online: 03 January 2022