Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images
| dc.authorid | 0000-0003-3672-1865 | en_US |
| dc.authorid | 0000-0003-1840-9958 | en_US |
| dc.authorid | 0000-0002-7201-6963 | en_US |
| dc.contributor.author | Alhudhaif, Adi | |
| dc.contributor.author | Polat, Kemal | |
| dc.contributor.author | Karaman, Onur | |
| dc.date.accessioned | 2023-05-29T12:45:35Z | |
| dc.date.available | 2023-05-29T12:45:35Z | |
| dc.date.issued | 2021 | en_US |
| dc.department | BAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü | en_US |
| dc.description.abstract | X-ray units have become one of the most advantageous candidates for triaging the new Coronavirus disease COVID-19 infected patients thanks to its relatively low radiation dose, ease of access, practical, reduced prices, and quick imaging process. This research intended to develop a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views. Moreover, it is aimed to prevent bias issues due to the database. Transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18, and SqueezeNet. The chest X-ray images were acquired from publicly available databases, and each individual image was carefully selected to prevent any bias problem. A stratified 5fold cross-validation approach was utilized with a ratio of 90% for training and 10% for the testing (unseen folds), in which 20% of training data was used as a validation set to prevent overfitting problems. The binary classification performances of the proposed CNN models were evaluated by the testing data. The activation mapping approach was implemented to improve the causality and visuality of the radiograph. The outcomes demonstrated that the proposed CNN model built on DenseNet-201 architecture outperformed amongst the others with the highest accuracy, precision, recall, and F1-scores of 94.96%, 89.74%, 94.59%, and 92.11%, respectively. The results indicated that the reliable diagnosis of COVID-19 pneumonia from CXIs based on the CNN model opens the door to accelerate triage, save critical time, and prioritize resources besides assisting the radiologists. | en_US |
| dc.description.sponsorship | This publication was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia. | en_US |
| dc.identifier.citation | Alhudhaif, A., Polat, K., & Karaman, O. (2021). Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images. Expert Systems with Applications, 180, 115141. | en_US |
| dc.identifier.doi | 10.1016/j.eswa.2021.115141 | |
| dc.identifier.issn | 0957-4174 | |
| dc.identifier.issn | 1873-6793 | |
| dc.identifier.pmid | 33967405 | en_US |
| dc.identifier.scopus | 2-s2.0-85105443130 | en_US |
| dc.identifier.scopusquality | Q1 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1016/j.eswa.2021.115141 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12491/10989 | |
| dc.identifier.volume | 180 | en_US |
| dc.identifier.wos | WOS:000663582300006 | en_US |
| dc.identifier.wosquality | Q1 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.indekslendigikaynak | PubMed | en_US |
| dc.institutionauthor | Polat, Kemal | |
| dc.language.iso | en | en_US |
| dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | en_US |
| dc.relation.ispartof | Expert Systems with Applications | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Corona Virus (COVID-19) | en_US |
| dc.subject | Convolutional Neural Network (CNN) | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Chest X-ray Images | en_US |
| dc.subject | Wuhan | en_US |
| dc.title | Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images | en_US |
| dc.type | Article | en_US |
