Improving detection and classification of diabetic retinopathy using CUDA and Mask RCNN

Basit Öğe Kaydı
dc.authoridErciyas, Abdussamed/0000-0003-4714-8523
dc.authoridPolat, Huseyin/0000-0003-4128-2625
dc.contributor.authorErciyas, Abdussamed
dc.contributor.authorBarisci, Necaattin
dc.contributor.authorUnver, Halil Murat
dc.contributor.authorPolat, Huseyin
dc.date.accessioned2025-01-21T16:41:42Z
dc.date.available2025-01-21T16:41:42Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractDiabetic retinopathy (DR) is an eye disease caused by diabetes and can progress to certain degrees. Because DR's the final stage can cause blindness, early detection is crucial to prevent visual disturbances. With the development of GPU technology, image classification and object detection can be done faster. Particularly on medical images, these processes play an important role in disease detection. In this work, we improved our previous work to detect diabetic retinopathy using Faster RCNN and attention layer. In the detection phase, firstly non-used area of DR image was extracted using compute unified device architecture with gradient-based edge detection method. Then Mask RCNN was used instead of faster region-based convolutional neural networks (Faster RCNN) to detect lesion areas more successful. With the proposed method, more successful results were obtained than the our previous work in DenseNet, MobileNet and ResNet networks. In addition, more successful results were obtained than other works in the literature in ACC and AUC metrics obtained by using VGG19.
dc.identifier.doi10.1007/s11760-022-02334-9
dc.identifier.endpage1273
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85136145027
dc.identifier.scopusqualityQ2
dc.identifier.startpage1265
dc.identifier.urihttps://doi.org/10.1007/s11760-022-02334-9
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24929
dc.identifier.volume17
dc.identifier.wosWOS:000840386600001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofSignal Image and Video Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectDiabetic retinopathy; Deep learning; Detection and classification; CUDA; GPU programming
dc.titleImproving detection and classification of diabetic retinopathy using CUDA and Mask RCNN
dc.typeArticle

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu