A new model to detect COVID-19 patients based on Convolution Neural Network via l1 regularization
Yazarlar (6)
Chrispin Jiji The Oxford College Of Engineering, Hindistan
Annie Bessant St. Xavier's Catholic College Of Engineering, Hindistan
K. Martin Sagayam
Karunya Institute Of Technology And Sciences, Hindistan
Karunya Institute Of Technology And Sciences, Hindistan
A. Amir Anton Jone
Kafkas Üniversitesi, Türkiye
Kafkas Üniversitesi, Türkiye
Doç. Dr. Hatıra GÜNERHAN Kafkas Üniversitesi, Türkiye
Alphonse Houwe Université De Maroua, Kamerun
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Applied Mathematics in Science and Engineering (Q2) | ||
| Dergi ISSN | 2769-0911 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2023 |
| Cilt / Sayı / Sayfa | 31 / 1 / 1–18 | DOI | 10.1080/27690911.2023.2220872 |
| Makale Linki | http://dx.doi.org/10.1080/27690911.2023.2220872 | ||
| UAK Araştırma Alanları |
Uygulamalı Matematik
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| 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 … |
| Anahtar Kelimeler |
| Accuracy | Convolution Neural Network | COVID-19 | exception | mobilenet | X-ray image |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 3 |
| Scopus | 4 |
| Google Scholar | 4 |