| Yazarlar |
Chrispin Jiji
|
|
Annie Bessant
|
|
Kulandairaj Martin Sagayam
|
|
A. Amir Anton Joned
|
Doç. Dr. Hatıra GÜNERHAN
Kafkas Üniversitesi, Türkiye |
|
Alphonse Houwe
|
| Özet |
| The 2019 new coronavirus illness (COVID-19) is an international public health emergency. Our social and healthcare systems are under a great deal of strain as a result of the daily increase in infection rates and fatalities. Doctors typically perform a chest X-ray to identify the diseased areas of the lungs since pneumonia is a common type of infection that spreads in the lungs. In this paper, we propose a Convolution Neural Network via the li regularization model to detect COVID-19 patients using chest X-Ray images. Due to the lack of the COVID-19 benchmark dataset, we use deep learning techniques to identify the best pre-trained CNN model for this job by comparing 15 models. The suggested algorithm was tested on 1316 photos (116 COVID-19 cases, 328 healthy controls, and 872 pneumonia cases), with 66% for training, 17% for validation, and 17% for testing. The classification accuracy, loss, value-accuracy, and value-loss values obtained by the suggested technique are 0.9912, 0.0187, 0.1119, and 0.9506 respectively. Additionally, the model effectively decreases training loss while boosting accuracy. The results show that proposed procedures are more effective than existing ones at identifying COVID-19 cases from chest X-ray pictures. |
| Anahtar Kelimeler |
| Accuracy | Convolution Neural Network | COVID-19 | exception | mobilenet | X-ray image |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | Applied Mathematics in Science and Engineering |
| Dergi ISSN | 2769-0911 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 06-2023 |
| Cilt No | 31 |
| Sayı | 1 |
| Sayfalar | 1 / 18 |
| Doi Numarası | 10.1080/27690911.2023.2220872 |
| Makale Linki | http://dx.doi.org/10.1080/27690911.2023.2220872 |
| Atıf Sayıları | |
| SCOPUS | 2 |
| Google Scholar | 2 |
