MaxGlaViT: A Novel Lightweight Vision Transformer‐Based Approach for Early Diagnosis of Glaucoma Stages From Fundus Images
Yazarlar (3)
Arş. Gör. Mustafa Yurdakul Kirikkale Üniversitesi, Türkiye
Dr. Öğr. Üyesi Kübra Uyar Alanya Alaaddin Keykubat University, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | International Journal of Imaging Systems and Technology (Q2) | ||
| Dergi ISSN | 0899-9457 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 07-2025 |
| Kabul Tarihi | 26-06-2025 | Yayınlanma Tarihi | 01-07-2025 |
| Cilt / Sayı / Sayfa | 35 / 4 / – | DOI | 10.1002/ima.70159 |
| Makale Linki | https://doi.org/10.1002/ima.70159 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| Glaucoma is a prevalent eye disease that often progresses without symptoms and can lead to permanent vision loss if not detected early. The limited number of specialists and overcrowded clinics worldwide make it difficult to detect the disease at an early stage. Deep learning‐based computer‐aided diagnosis (CAD) systems are a solution to this problem, enabling faster and more accurate diagnosis. In this study, we proposed MaxGlaViT, a novel Vision Transformer model based on MaxViT to diagnose different stages of glaucoma. The architecture of the model is constructed in three steps: (i) the Multi Axis Vision Transformer (MaxViT) structure is scaled in terms of the number of blocks and channels, (ii) low‐level feature extraction is improved by integrating the attention mechanism into the stem block, and (iii) high‐level feature extraction is improved by using the modern convolutional structure. The MaxGlaViT … |
| Anahtar Kelimeler |
| ConvNeXtV2 | ECA | eye diseases | glaucoma diagnosis | MaxViT | vision transformer |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 15 |
| Scopus | 16 |
| Google Scholar | 29 |