ROPGCViT: A Novel Explainable Vision Transformer for Retinopathy of Prematurity Diagnosis
Yazarlar (4)
Arş. Gör. Mustafa Yurdakul Kirikkale Üniversitesi, Türkiye
Dr. Öğr. Üyesi Kübra Uyar Alanya Alaaddin Keykubat University, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
Dr. Öğr. Üyesi İrfan Atabaş Kirikkale Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | IEEE Access (Q2) | ||
| Dergi ISSN | 2169-3536 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 04-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-01-2025 |
| Cilt / Sayı / Sayfa | 13 / 1 / 77064–77079 | DOI | 10.1109/ACCESS.2025.3564213 |
| Makale Linki | https://ieeexplore.ieee.org/abstract/document/10975756 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Retinopathy of Prematurity (ROP) is a severe disease that occurs in premature babies due to abnormal development of retinal vessels and can lead to permanent vision loss. Fundus images are critical in the diagnosis of ROP; however, the examination of fundus images is a subjective, time-consuming, and error-prone process that requires experience. This situation can lead to delayed diagnosis and inaccurate evaluations. Therefore, the need for computer-aided diagnosis (CAD) systems is increasing day by day. Deep learning (DL) methods have a high potential in analyzing such complex images. In this study, a total of 50 DL models, 25 Convolutional Neural Network (CNN), and 25 Vision Transformer (ViT) models were tested to diagnose ROP from fundus images. Furthermore, the ROPGCViT model based on the Global Context Vision Transformer (GCViT) was proposed. GCViT was enhanced with Squeeze … |
| Anahtar Kelimeler |
| convolutional neural network | deep learning | grad-CAM | Retinopathy of prematurity | squeeze-and-excitation | vision transformer |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 11 |
| Scopus | 16 |
| Google Scholar | 18 |