Derin Öğrenme Kullanılarak Fundus Görüntülerinden Katarakt ve Diyabetik Retinopati Tespiti

Year 2023, Volume: 5 Issue: 2, 312 - 324, 27.10.2023

Şükrü Aykat , Sibel Senan

https://doi.org/10.46387/bjesr.1332567

Abstract

Diyabetik retinopati ve katarakt ciddi körlüğe ve görme kaybına neden olabilen bazı retina hastalıklarıdır. Gözde meydana gelen bu geri dönüşü olmayan hasarı önlemek için retina hastalıklarının erken teşhisi hayati önem taşımaktadır. Bu çalışmanın problem cümlesi, bu retina hastalıklarının tespiti için derin öğrenme tabanlı sonuçların sunulması olarak verilebilir. Bu amaçla ilk önce ham bir veri seti üzerinde histogram eşitleme yöntemi kullanılarak yeni bir seti oluşturulmuştur. Ardından beş geleneksel derin öğrenme modeline hiperparametre ayarı yapılarak veri setleri üzerinde eğitimler gerçekleştirilmiştir. En son olarak veri setleri üzerinde en yüksek başarıya sahip MobileNet tabanlı bir hibrit model geliştirilmiştir. Önerilen hibrit model, ön işlenmiş veri seti üzerinde %99 doğruluk oranı elde etmiştir. Hibrit modelin sınıflandırma başarısının literatürdeki derin öğrenme modellerinin başarısından daha yüksek olduğu görülmüştür. Bu çalışma diyabetik retinopati ve katarakt hastalarının teşhis sürecine katkı sağlayacaktır.

Keywords

Retina Hastalıkları, Derin Öğrenme, Evrişimli Sinir Ağları, Katarakt, Diyabetik Retinopati

Detection of Cataract and Diabetic Retinopathy from Fundus Images Using Deep Learning

Abstract

Diabetic retinopathy and cataract are some retinal diseases that can cause severe blindness and vision loss. Early diagnosis of retinal diseases is vital to prevent this irreversible damage to the eye. The problem statement of this study can be given as the presentation of deep learning-based results for the detection of these retinal diseases. For this purpose, firstly, a new set was created using the histogram equalization method on a raw data set. Then, hyperparameter adjustments were made to five traditional deep learning models and training was carried out on the data sets. Finally, a MobileNet-based hybrid model with the highest success on datasets has been developed. The proposed hybrid model achieved 99% accuracy on the preprocessed dataset. It has been observed that the classification success of the hybrid model is higher than the success of the deep learning models in the literature. This study will contribute to the diagnosis process of diabetic retinopathy and cataract patients.

Keywords

Retinal Diseases, Deep Learning, Convolutional Neural Networks, Cataract, Diabetic Retinopathy

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