COMPARATIVE ANALYSIS OF VISION TRANSFORMERS AND CONVOLUTIONAL NEURAL NETWORKS IN DIABETIC RETINOPATHY DIAGNOSIS

Esra Yüzgeç Özdemir; Canan Koç; Fatih Özyurt

doi:10.17780/ksujes.1521858

EN TR

COMPARATIVE ANALYSIS OF VISION TRANSFORMERS AND CONVOLUTIONAL NEURAL NETWORKS IN DIABETIC RETINOPATHY DIAGNOSIS

Abstract

Diabetic retinopathy can lead to significant visual complications and significantly affects individuals' quality of life. This study focuses on comparing the performance of Vision Transformer (ViT) models and Convolutional Neural Networks (CNN) methods in diabetic retinopathy diagnosis and aims to evaluate their potential as an alternative to traditional diagnostic methods. In this study, the performance of four different ViT model architectures and four different convolutional neural network (CNN) models in training and testing phases were comparatively analyzed. ViT models achieved accuracy rates of 97.83%, 98.41%, 95.2%, and 98.26% for "tiny," "base," "small," and "large," respectively. Additionally, models trained with VGG13, ResNet18, ResNet50, and SqueezeNet architectures from CNN techniques achieved accuracy rates of 96.1%, 97.83%, 90.9%, and 93.93%, respectively. ViT architectures achieved higher accuracy rates than CNN architectures. When the results were evaluated, it was concluded that ViT methods were more successful in the diagnosis of diabetic retinopathy.

Keywords

GÖRÜNTÜ TRANSFORMATÖRLERİ VE EVRİŞİMLİ SİNİR AĞLARININ DİYABETİK RETİNOPATİ TEŞHİSİNDE KARŞILAŞTIRMALI ANALİZİ

Öz

Diyabetik retinopati, önemli görsel komplikasyonlara yol açabilen ve bireylerin yaşam kalitesini önemli ölçüde etkileyen bir hastalıktır. Bu çalışma, diyabetik retinopatinin erken evrelerde teşhis edilmesinin önemini vurgulamakta, mevcut teşhis yöntemlerinin sınırlılıklarına dikkat çekmekte ve geleneksel yöntemlere alternatif olarak Görüntü Dönüştürücüsü (ViT) modellerinin potansiyelini ele almaktadır. Bu çalışmada, dört farklı ViT model mimarisinin yanı sıra döt farklı evrişimli sinir ağı (CNN) modellerinin eğitim ve test aşamalarındaki performansları karşılaştırmalı olarak analiz edilmiştir. ViT modelleri 'tiny', 'base', 'small' ve 'large' sırasıyla %97,83, %98,41, %95,2 ve %98,26 doğruluk oranlarına ulaşmıştır. Ayrıca CNN tekniklerinden VGG13, ResNet18, ResNet50 ve SqueezeNet mimarileri ile eğitilen modeller sırasıyla %96,1, %97,83, %90,9 ve %93,93 doğruluk oranlarına ulaşmıştır. Çalışma sonucunda ViT mimarileri CNN mimarilerine göre daha yüksek doğruluk oranlarına ulaşmıştır. Sonuçlar değerlendirildiğinde ViT yöntemlerinin diyabetik retinopati teşhisinde daha başarılı olduğu sonucuna varılmıştır.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Yazılım Mühendisliği (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Esra Yüzgeç Özdemir ^*
0000-0003-2914-2603
Türkiye

Canan Koç
0000-0002-2651-9471
Türkiye

Fatih Özyurt
0000-0002-8154-6691
Türkiye

Yayımlanma Tarihi

3 Haziran 2025

Gönderilme Tarihi

24 Temmuz 2024

Kabul Tarihi

23 Ocak 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 2

DOI

https://doi.org/10.17780/ksujes.1521858

IZ

https://izlik.org/JA72XY35EG

Kaynak Göster

RIS / Bibtex

APA

Yüzgeç Özdemir, E., Koç, C., & Özyurt, F. (2025). COMPARATIVE ANALYSIS OF VISION TRANSFORMERS AND CONVOLUTIONAL NEURAL NETWORKS IN DIABETIC RETINOPATHY DIAGNOSIS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(2), 592-600. https://doi.org/10.17780/ksujes.1521858