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Bach, Tran Xuan
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A novel approach for detecting diabetic retinopathy using two-stream CNNs model Viet Huong, Pham Thi; Thinh, Le Duc; Oanh, Tran Thi; Bach, Tran Xuan; Huy, Hoang Quang; Vu, Tran Anh
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp200-209

Abstract

Major causes of visual impairment, particularly diabetic retinopathy (DR) and aged-related macular degeneration (AMD), has posed significant challenges for clinical diagnosis and treatment. Early detection and prompt intervention can help prevent severe consequences for patients. The study presents a novel approach for detecting eye diseases using a two-stream convolutional neural network (CNN) model. The first stream processes preprocessed fundus images, while the second stream analyzes high-pass filtered fundus images in the spatial frequency domain. To assess the model’s performance, we use the APTOS 2019 dataset, which was originally compiled for the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection competition and is publicly available on Kaggle. Our method shows promise as an early screening tool for DR detection with an accuracy of 0.986.
Co-Authors Huy, Hoang Quang Oanh, Tran Thi Thinh, Le Duc Viet Huong, Pham Thi Vu, Tran Anh