A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images
Lung infection represents one of the most perilous indicators of Covid-19. The most efficient diagnostic approach entails the analysis of CT scan images. Utilizing deep learning algorithms and machine vision, computer scientists have devised a method for automated detection of this disease. This stu...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of science and culture
2023-07-01
|
| Series: | International Journal of Web Research |
| Subjects: | |
| Online Access: | https://ijwr.usc.ac.ir/article_197180_ca24efdc59c110e9cfdd5830269b4608.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846107911838760960 |
|---|---|
| author | Touba torabipour Abolfazl Gandomi Mohammad Ghanimi |
| author_facet | Touba torabipour Abolfazl Gandomi Mohammad Ghanimi |
| author_sort | Touba torabipour |
| collection | DOAJ |
| description | Lung infection represents one of the most perilous indicators of Covid-19. The most efficient diagnostic approach entails the analysis of CT scan images. Utilizing deep learning algorithms and machine vision, computer scientists have devised a method for automated detection of this disease. This study proposes a two-stage approach to identifying lung infection. In the initial stage, image features are extracted through a transfer learning framework employing ResNet50, with the last two layers being fixed. Subsequently, a CNN neural network is constructed for image detection and categorization in the second stage. By employing superior image feature selection and minimizing non-informative features, this proposed method achieves impressive accuracy metrics: 98.99% accuracy, 98.91% sensitivity, and 99.10% specificity. Furthermore, a comparative analysis is conducted between this method and six other architectures (Inception, InceptionResNetV2, ResNet101, ResNet152, VGG16, VGG19), with and without transfer learning. The findings demonstrate that the proposed method attains 98% accuracy on test data, without succumbing to overfitting. |
| format | Article |
| id | doaj-art-227bf38e762a44e2b1df5d472b204ba1 |
| institution | Kabale University |
| issn | 2645-4343 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-227bf38e762a44e2b1df5d472b204ba12024-12-26T08:02:06ZengUniversity of science and cultureInternational Journal of Web Research2645-43432023-07-016213314210.22133/ijwr.2024.449726.1211A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan ImagesTouba torabipour0Abolfazl Gandomi 1Mohammad Ghanimi2Department of Computer, Yazd Branch, Islamic Azad University, Yazd, IranDepartment of Computer, Yazd Branch, Islamic Azad University, Yazd, IranDepartment of Computer, Ershad Damavand University, Tehran, IranLung infection represents one of the most perilous indicators of Covid-19. The most efficient diagnostic approach entails the analysis of CT scan images. Utilizing deep learning algorithms and machine vision, computer scientists have devised a method for automated detection of this disease. This study proposes a two-stage approach to identifying lung infection. In the initial stage, image features are extracted through a transfer learning framework employing ResNet50, with the last two layers being fixed. Subsequently, a CNN neural network is constructed for image detection and categorization in the second stage. By employing superior image feature selection and minimizing non-informative features, this proposed method achieves impressive accuracy metrics: 98.99% accuracy, 98.91% sensitivity, and 99.10% specificity. Furthermore, a comparative analysis is conducted between this method and six other architectures (Inception, InceptionResNetV2, ResNet101, ResNet152, VGG16, VGG19), with and without transfer learning. The findings demonstrate that the proposed method attains 98% accuracy on test data, without succumbing to overfitting.https://ijwr.usc.ac.ir/article_197180_ca24efdc59c110e9cfdd5830269b4608.pdfnatural networkconvolutiondeep learningcovid 19ct scan radiographs |
| spellingShingle | Touba torabipour Abolfazl Gandomi Mohammad Ghanimi A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images International Journal of Web Research natural network convolution deep learning covid 19 ct scan radiographs |
| title | A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images |
| title_full | A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images |
| title_fullStr | A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images |
| title_full_unstemmed | A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images |
| title_short | A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images |
| title_sort | two stage method for diagnosing covid 19 leveraging cnn and transfer learning on ct scan images |
| topic | natural network convolution deep learning covid 19 ct scan radiographs |
| url | https://ijwr.usc.ac.ir/article_197180_ca24efdc59c110e9cfdd5830269b4608.pdf |
| work_keys_str_mv | AT toubatorabipour atwostagemethodfordiagnosingcovid19leveragingcnnandtransferlearningonctscanimages AT abolfazlgandomi atwostagemethodfordiagnosingcovid19leveragingcnnandtransferlearningonctscanimages AT mohammadghanimi atwostagemethodfordiagnosingcovid19leveragingcnnandtransferlearningonctscanimages AT toubatorabipour twostagemethodfordiagnosingcovid19leveragingcnnandtransferlearningonctscanimages AT abolfazlgandomi twostagemethodfordiagnosingcovid19leveragingcnnandtransferlearningonctscanimages AT mohammadghanimi twostagemethodfordiagnosingcovid19leveragingcnnandtransferlearningonctscanimages |