Covid-19 detection on x-ray images using a deep learning architecture
Joint Authors
Akgul, Ismail
Kaya, Volkan
Unver, Edhem
Karavas, Erdal
Baran, Ahmad
Tuncer, Servet
Source
Journal of Engineering Research
Issue
Vol. 11, Issue 2 B (30 Jun. 2023), pp.15-26, 12 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2023-06-30
Country of Publication
Kuwait
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
Coronavirus disease (Covid-19) has recently emerged as a serious public health threat, spreading rapidly worldwide and threatening millions of lives.
With an increasing number of cases and mutations, medical resources are being drained daily owing to the rapid transmission of the disease, and the health systems of many countries are negatively affected.
Therefore, it is important to use the available resources appropriately and in a timely manner to detect and treat the disease.
In this study, VGG16 and ResNet50 deep learning models were used to quickly evaluate x-ray images and perform a prediagnosis of Covid-19, and an alternative model (IsVoNet) was proposed.
Following model training, success accuracies of 99.92%, 99.65%, and 99.76% were achieved in the VGG16 model, ResNet50 model, and proposed model, respectively.
According to the results, the models classified the Covid-19 and normal lung x-ray images with high accuracy, and the proposed model showed a high success rate at a lower time complexity than the other models
American Psychological Association (APA)
Akgul, Ismail& Kaya, Volkan& Unver, Edhem& Karavas, Erdal& Baran, Ahmad& Tuncer, Servet. 2023. Covid-19 detection on x-ray images using a deep learning architecture. Journal of Engineering Research،Vol. 11, no. 2 B, pp.15-26.
https://search.emarefa.net/detail/BIM-1604337
Modern Language Association (MLA)
Akgul, Ismail…[et al.]. Covid-19 detection on x-ray images using a deep learning architecture. Journal of Engineering Research Vol. 11, no. 2 B (Jun. 2023), pp.15-26.
https://search.emarefa.net/detail/BIM-1604337
American Medical Association (AMA)
Akgul, Ismail& Kaya, Volkan& Unver, Edhem& Karavas, Erdal& Baran, Ahmad& Tuncer, Servet. Covid-19 detection on x-ray images using a deep learning architecture. Journal of Engineering Research. 2023. Vol. 11, no. 2 B, pp.15-26.
https://search.emarefa.net/detail/BIM-1604337
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 24-25
Record ID
BIM-1604337