Darıcı, Muazzez BuketDarici,M.B.Yiğit, GülsümYigit,G.2024-06-232024-06-2320231979-835034081-5https://doi.org/10.1109/UBMK59864.2023.10286722https://hdl.handle.net/20.500.12469/5862This study addresses Diabetic Retinopathy (DR), a diabetes complication that can lead to vision loss if not promptly diagnosed and treated. Recent advances in deep learning have shown promising results in detecting DR from retinal images. The study introduces a novel patch-based CNN-biGRU model for DR detection. The proposed model extracts patches from retinal images employing a sliding window strategy and then uses a Convolutional Neural Network (CNN) architecture to extract features from each patch. The features extracted from each patch are then concatenated, and a 4-layer bidirectional Gated Recurrent Unit (biGRU) is applied to predict the whole image. We assessed the proposed model on a publicly available dataset named APTOS 2019 Blindness Detection and achieved an accuracy of 73.5%, outperforming existing state-of-the-art approaches. The given patch-based CNN model can improve the accuracy of DR detection and aims to assist ophthalmologists in making more accurate diagnoses. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessautomatic diagnosisbiGRUCNNsdiabetic retinopathyGRUpatched-based approachConference Object61010.1109/UBMK59864.2023.102867222-s2.0-85177597833N/AN/A