ArticleInternational journal of imaging systems and technology2022
COVID-opt-aiNet: A clinical decision support system for COVID-19 detection.
Article in International journal of imaging systems and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods.Health science reports · 2024Article
- Nature-Inspired Intelligent Computing: A Comprehensive Survey.Research (Washington, D.C.) · 2024Review
- Clinical decision support systems for diabetic foot ulcers: a scoping review.Revista da Escola de Enfermagem da U S P · 2024Article
- Harnessing the power of clinical decision support systems: challenges and opportunities.Open heart · 2023Review
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- COVID-opt-aiNet: A clinical decision support system for COVID-19 detection.International journal of imaging systems and technology · 2022Article
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
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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.
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