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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of biomolecular structure & dynamics Jg. 42; H. 11; S. 5699 - 5712
Hauptverfasser: Merrouchi, M., Benyoussef, Y., Skittou, M., Atifi, K., Gadi, T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Taylor & Francis 23.07.2024
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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
Bibliographie: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