ConvCoroNet: a deep convolutional neural network optimized with iterative thresholding algorithm for Covid-19 detection using chest X-ray images

Covid-19 is a global pandemic. Early and accurate detection of positive cases prevent the further spread of this epidemic and help to treat rapidly the infected patients. During the peak of this epidemic, there was an insufficiency of Covid-19 test kits. In addition, this technique takes a considera...

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Vydané v:	Journal of biomolecular structure & dynamics Ročník 42; číslo 11; s. 5699 - 5712
Hlavní autori:	Merrouchi, M., Benyoussef, Y., Skittou, M., Atifi, K., Gadi, T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Taylor & Francis 23.07.2024
Predmet:	Algorithms ConvCoroNet convolutional neural network (CNNs) covid-19 COVID-19 - diagnosis COVID-19 - diagnostic imaging COVID-19 - virology deep learning Humans iterative thresholding algorithm based optimizer Neural Networks, Computer Radiography, Thoracic - methods SARS-CoV-2 Sensitivity and Specificity support vector machine (SVM) X-ray chest images convolutional neural network (CNNs) deep learning iterative thresholding algorithm based optimizer support vector machine (SVM) covid-19 ConvCoroNet X-ray chest images
ISSN:	0739-1102, 1538-0254, 1538-0254
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Popis
Shrnutí:	Covid-19 is a global pandemic. Early and accurate detection of positive cases prevent the further spread of this epidemic and help to treat rapidly the infected patients. During the peak of this epidemic, there was an insufficiency of Covid-19 test kits. In addition, this technique takes a considerable time in the diagnosis. Hence the need to find fast, accurate and low-cost method to replace or supplement RT PCR-based methods. Covid-19 is a respiratory disease, chest X-ray images are often used to diagnose pneumonia. From this perspective, these images can play an important role in the Covid-19 detection. In this article, we propose ConvCoroNet, a deep convolutional neural network model optimized with new method based on iterative thresholding algorithm to detect coronavirus automatically from chest X-ray images. ConvCoroNet is trained on a dataset prepared by collecting chest X-ray images of Covid-19, pneumonia and normal cases from publically datasets. The experimental results of our proposed model show a high accuracy of 99.50%, sensitivity of 98.80% and specificity of 99.85% when detecting Covid-19 from chest X-ray images. ConvCoroNet achieves promising results in the automatic detection of Covid-19 from chest X-ray images. It may be able to help radiologists in the Covid-19 detection by reducing the examination time of X-ray images. Communicated by Ramaswamy H. Sarma
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0739-1102 1538-0254 1538-0254
DOI:	10.1080/07391102.2023.2227726