A DEEP TRANSFER LEARNING FRAMEWORK for the STAGING of DIABETIC RETINOPATHY

Authors : Gökalp ÇINARER, Kazım KILIÇ, Tuba PARLAR

Pages : 106-119

View : 24 | Download : 8

Publication Date : 2022-12-31

Article Type : Research Paper

Abstract :Diabetes is a highly prevalent and increasingly common health disorder, resulting in health complications such as vision loss. Diabetic retinopathy insert ignore into journalissuearticles values(DR); is the most common form of diabetes-caused eye disease. Early diagnosis and treatment are crucial to prevent vision loss. DR is a progressive disease composed of five stages. The accurate diagnosis of DR stages is highly important in guiding the treatment process. In this study, we propose a deep transfer learning framework for automatic detection of DR stages. We examine our proposed model by comparing different convolutional neural networks architectures: VGGNet19, DenseNet201, and ResNet152. Our results demonstrate better accuracy after applying transfer learning and hyper-parameter tuning to classify the fundus images. When the general test accuracy and the performance evaluations are compared, the DenseNet201 model is observed with the highest test accuracy of 82.7%. Among the classification algorithms, the highest AUC value is 94.1% obtained with RestNet152.
Keywords : Convolutional neural networks, CNNs, Deep learning, Diabetic retinopathy, Transfer learning

ORIGINAL ARTICLE URL