Detection of COVID-19 Using a Pre-trained CNN Model Over Chest X-ray Images
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,...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of science and culture
2022-07-01
|
| Series: | International Journal of Web Research |
| Subjects: | |
| Online Access: | https://ijwr.usc.ac.ir/article_166118_6450f46a7d04195b5f258b2b94c81e2b.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841560261383684096 |
|---|---|
| author | Mohammadreza Behnia Touba Torabipour Safieh Siadat |
| author_facet | Mohammadreza Behnia Touba Torabipour Safieh Siadat |
| author_sort | Mohammadreza Behnia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-370cc0aa7df34e59a74b2d659c010fd5 |
| institution | Kabale University |
| issn | 2645-4343 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-370cc0aa7df34e59a74b2d659c010fd52025-01-04T11:39:12ZengUniversity of science and cultureInternational Journal of Web Research2645-43432022-07-015210311210.22133/ijwr.2023.375263.1145Detection of COVID-19 Using a Pre-trained CNN Model Over Chest X-ray ImagesMohammadreza Behnia0Touba Torabipour1Safieh Siadat2Department of Computer Engineering and Information Technology, International Azad University IranDepartment of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran, IranDepartment of Computer Engineering and Information Technology, Payame Noor University (PNU), Tehran, IranLung 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.https://ijwr.usc.ac.ir/article_166118_6450f46a7d04195b5f258b2b94c81e2b.pdfconvolutional neural networkdeep learningchest x-raycovid-19 |
| spellingShingle | Mohammadreza Behnia Touba Torabipour Safieh Siadat Detection of COVID-19 Using a Pre-trained CNN Model Over Chest X-ray Images International Journal of Web Research convolutional neural network deep learning chest x-ray covid-19 |
| title | Detection of COVID-19 Using a Pre-trained CNN Model Over Chest X-ray Images |
| title_full | Detection of COVID-19 Using a Pre-trained CNN Model Over Chest X-ray Images |
| title_fullStr | Detection of COVID-19 Using a Pre-trained CNN Model Over Chest X-ray Images |
| title_full_unstemmed | Detection of COVID-19 Using a Pre-trained CNN Model Over Chest X-ray Images |
| title_short | Detection of COVID-19 Using a Pre-trained CNN Model Over Chest X-ray Images |
| title_sort | detection of covid 19 using a pre trained cnn model over chest x ray images |
| topic | convolutional neural network deep learning chest x-ray covid-19 |
| url | https://ijwr.usc.ac.ir/article_166118_6450f46a7d04195b5f258b2b94c81e2b.pdf |
| work_keys_str_mv | AT mohammadrezabehnia detectionofcovid19usingapretrainedcnnmodeloverchestxrayimages AT toubatorabipour detectionofcovid19usingapretrainedcnnmodeloverchestxrayimages AT safiehsiadat detectionofcovid19usingapretrainedcnnmodeloverchestxrayimages |