EYE DISEASE DETECTION WITH DEEP LEARNING MODELS SUPPORTED BY THE CBAM ATTENTION MECHANISM

Rıdvan Coşkun; Duygu Kaya; Hasan Güler

doi:10.17780/ksujes.1759068

EN TR

EYE DISEASE DETECTION WITH DEEP LEARNING MODELS SUPPORTED BY THE CBAM ATTENTION MECHANISM

Abstract

Early diagnosis of eye diseases plays a critical role in treatment success and public health. With the widespread use of modern medical imaging methods, the development of automated diagnostic systems from retinal fundus images has become an important research area. In this study, the effects of integrating the Convolutional Block Attention Module (CBAM) into EfficientNetB0 and DenseNet121 architectures were investigated for the classification of cataract, diabetic retinopathy, glaucoma, and healthy subjects. Experimental results demonstrated that the CBAM attention mechanism enhances accuracy and generalization performance, particularly in distinguishing complex retinal findings. For DenseNet121, accuracy, precision, recall, and F1-score were obtained as 88.37%, 89.66%, 88.37%, and 88.52%, respectively. EfficientNetB0 achieved 96.32% accuracy, 96.34% precision, 96.32% recall, and 96.33% F1-score. After CBAM integration, the accuracy of DenseNet121 increased to 90.39% and its F1-score to 90.54%, while EfficientNetB0 improved to 96.56% accuracy and 96.57% F1-score. These results reveal that the incorporation of CBAM enhances the performance of deep learning models and significantly contributes to the development of reliable and clinically applicable systems for the automated detection of eye diseases

Keywords

CBAM DİKKAT MEKANİZMASI İLE DESTEKLENMİŞ DERİN ÖĞRENME MODELLERİYLE GÖZ HASTALIKLARININ TESPİTİ

Öz

Göz hastalıklarının erken teşhisi, tedavi başarısı ve toplum sağlığı açısından kritik bir rol oynamaktadır. Modern tıbbi görüntüleme yöntemlerinin yaygınlaşmasıyla birlikte, retina fundus görüntülerinden otomatik tanı sistemlerinin geliştirilmesi önemli bir araştırma alanı hâline gelmiştir. Bu çalışmada, katarakt, diyabetik retinopati, glokom ve sağlıklı bireylerin sınıflandırılması amacıyla EfficientNetB0 ve DenseNet121 mimarileri ile Convolutional Block Attention Module (CBAM) entegrasyonunun etkileri incelenmiştir. Deneysel sonuçlar, CBAM dikkat mekanizmasının özellikle karmaşık retinal bulguların ayırt edilmesinde doğruluk ve genelleme performansını artırdığını göstermiştir. DenseNet121 modeli için doğruluk %88,37, kesinlik %89,66, duyarlılık %88,37 ve F1 skoru %88,52 olarak elde edilmiştir. EfficientNetB0 modeli ise %96,32 doğruluk, %96,34 kesinlik, %96,32 duyarlılık ve %96,33 F1 skoruna ulaşmıştır. CBAM entegrasyonu sonrası DenseNet121’in doğruluğu %90,39’a, F1 skoru %90,54’e; EfficientNetB0’un doğruluğu %96,56’ya, F1 skoru ise %96,57’ye yükselmiştir. Sonuçlar, CBAM entegrasyonunun derin öğrenme modellerinin başarımını artırdığını ve otomatik göz hastalığı tespitinde güvenilir, klinik olarak uygulanabilir sistemlerin geliştirilmesine önemli katkı sağladığını ortaya koymaktadır.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Elektrik Mühendisliği (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Rıdvan Coşkun ^*
0000-0002-3673-6306
Türkiye

Duygu Kaya
0000-0002-6453-631X
Türkiye

Hasan Güler
0000-0002-9917-3619
Türkiye

Yayımlanma Tarihi

3 Aralık 2025

Gönderilme Tarihi

6 Ağustos 2025

Kabul Tarihi

14 Kasım 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 4

DOI

https://doi.org/10.17780/ksujes.1759068

IZ

https://izlik.org/JA55NA89ZJ

Kaynak Göster

RIS / Bibtex

APA

Coşkun, R., Kaya, D., & Güler, H. (2025). EYE DISEASE DETECTION WITH DEEP LEARNING MODELS SUPPORTED BY THE CBAM ATTENTION MECHANISM. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(4), 1983-1999. https://doi.org/10.17780/ksujes.1759068