A DEEP TRANSFER LEARNING FRAMEWORK for the STAGING of DIABETIC RETINOPATHY
| dc.contributor.author | Çınarer, Gökalp | |
| dc.contributor.author | Kılıç, Kazım | |
| dc.contributor.author | Parlar, Tuba | |
| dc.date.accessioned | 2024-09-19T16:28:12Z | |
| dc.date.available | 2024-09-19T16:28:12Z | |
| dc.date.issued | 2022 | |
| dc.department | Hatay Mustafa Kemal Üniversitesi | en_US |
| dc.description.abstract | Diabetes is a highly prevalent and increasingly common health disorder, resulting in health complications such as vision loss. Diabetic retinopathy (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. | en_US |
| dc.identifier.endpage | 119 | en_US |
| dc.identifier.issn | 2687-6167 | |
| dc.identifier.issue | 051 | en_US |
| dc.identifier.startpage | 106 | en_US |
| dc.identifier.trdizinid | 1151899 | en_US |
| dc.identifier.uri | https://search.trdizin.gov.tr/tr/yayin/detay/1151899 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12483/16592 | |
| dc.indekslendigikaynak | TR-Dizin | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | Journal of scientific reports-A (Online) | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Convolutional neural networks | en_US |
| dc.subject | CNNs | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Diabetic retinopathy | en_US |
| dc.subject | Transfer learning | en_US |
| dc.title | A DEEP TRANSFER LEARNING FRAMEWORK for the STAGING of DIABETIC RETINOPATHY | en_US |
| dc.type | Article | en_US |
