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

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


Summrina Kanwal

Faiza Khan

Sultan Alamri

Kia Dashtipur


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:

Journal Article Type Article
Acceptance Date Dec 11, 2021
Online Publication Date Jan 3, 2022
Publication Date 2022
Deposit Date Feb 9, 2022
Publicly Available Date Jan 4, 2023
Journal International Journal of Imaging Systems and Technology
Print ISSN 0899-9457
Electronic ISSN 1098-1098
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 32
Issue 2
Pages 444-461
Keywords bidirectional long-short-term memory, clinical decision support system, convolution neural network, COVID-19, deep learning neural network, feature selection, optimized artificial immune network, support vector machine
Public URL
Publisher URL


COVID‐opt‐aiNet: A Clinical Decision Support System For COVID‐19 Detection (accepted version) (463 Kb)

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