Detection of COVID-19 Using a Pre-trained CNN Model Over Chest X-ray Images

Document Type : Original Article

Authors

1 Department of Computer Engineering and Information Technology, International Azad University Iran

2 Department of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran, Iran

Abstract

Lung infection is the most dangerous sign of Covid 19. X-ray images are the most effective means of diagnosing this virus. In order to detect this disease, deep learning algorithms and machine vision are widely used by computer scientists. Convolutional neural networks (CNN), DenseNet121, Resnet50, and VGG16 were used in this study for the detection of Covid-19 in X-ray images. In the current study, 1341 chest radiographs from the COVID-19 dataset were used to detect COVID-19 including infected and Healthy classes using a modified pre-trained CNN (train and test accuracy of 99.75% and 99.63%, respectively). The DENSENET121 model has a training accuracy of 43.89% and a test accuracy of 57.89%, respectively. The train and test accuracy of ResNet-50 are, respectively, 89.43% and 90%. Additionally, the CNN model has test and train accuracy of 98.13% and 96.73%, respectively. The suggested model has COVID-19 detection accuracy that is at least 1% higher than all other models.

Keywords

References

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 Mohammad Reza Behnia completed his bachelor's degree in computer software engineering from Bandar Abbas Azad University in 2016, and he received a master's degree in the same field in 2021 at Qeshm Azad University, International Branch.

His field of activity is in the development of applications and the design of computer system architecture. He has designed several applications in these fields.

 Touba Torabipour has a bachelor's degree, and a master's degree in computer engineering, majoring in software engineering. Her areas of interest include Deep learning, reinforcement learning, machine vision, artificial intelligence, and the Internet of Things. She has published more than 6 articles in prestigious Iranian and international journals and conferences.

 Seyedeh Safieh Siadat, Assistant Professor, at Payame Noor University, has a bachelor's, master's, and doctoral degree in computer engineering majoring in software engineering. She has been a full-time faculty member at Payame Noor University since 1996. His areas of interest include the Internet of Things, game theory, machine learning, and cloud computing. She has published more than 42 articles in prestigious international journals and conferences

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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International Journal of Web Research
Volume 5, Issue 2
Summer-Autumn
December 2022
Pages 103-112

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