| Yazarlar |
Dr. Öğr. Üyesi Muhammed Akif YENİKAYA
Kafkas Üniversitesi, Türkiye |
Doç. Dr. Gökhan KERSE
Kafkas Üniversitesi, Türkiye |
Dr. Öğr. Üyesi Onur OKTAYSOY
Kafkas Üniversitesi, Türkiye |
| Özet |
| Artificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence. In the study, deep learning networks ResNet101, AlexNet, GoogLeNet, and Xception were considered, and it was aimed to determine the success of these networks in disease diagnosis. For this purpose, a dataset of 1,680 chest X-ray images was utilized, consisting of cases of COVID-19, viral pneumonia, and individuals without these diseases. These images were obtained by employing a rotation method to generate replicated data, wherein a split of 70 and 30% was adopted for training and validation, respectively. The analysis findings revealed that the deep learning networks were successful in classifying COVID-19, Viral Pneumonia, and Normal (disease-free) images. Moreover, an examination of the success levels revealed that the ResNet101 deep learning network was more successful than the others with a 96.32% success rate. In the study, it was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers. |
| Anahtar Kelimeler |
| healthcare sector | healthcare organizations | artificial intelligence | deep learning | COVID-19 | viral pneumonia |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | FRONTIERS IN PUBLIC HEALTH |
| Dergi ISSN | 2296-2565 |
| Dergi Tarandığı Indeksler | SSCI |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 04-2024 |
| Cilt No | 12 |
| Sayı | 2024 |
| Doi Numarası | 10.3389/fpubh.2024.1386110 |
| Makale Linki | https://www.frontiersin.org/journals/public-health/volumes?volume-id=1253 |
| Atıf Sayıları |
