Ensemble-based eye disease detection system utilizing fundus and vascular structures

• Published in: Scientific reports Vol. 15; no. 1; pp. 19298 - 16
• Main Authors: Yu, Hongjie, Dong, Xingbo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.06.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 692/699/3161; Accuracy; Algorithms; Blindness; Blood vessel segmentation; Deep learning; Diabetes mellitus; Diabetic Retinopathy - diagnosis; Diabetic Retinopathy - diagnostic imaging; Disease; Disease detection; Dual-branch Input; Ensemble learning; Eye diseases; Fundus Oculi; Humanities and Social Sciences; Humans; Image processing; Image Processing, Computer-Assisted - methods; Machine Learning; multidisciplinary; Retina; Retina - diagnostic imaging; Retinal disease classification; Retinal Diseases - diagnosis; Retinal Diseases - diagnostic imaging; Retinal Vessels - diagnostic imaging; Retinal Vessels - pathology; Retinopathy; Science; Science (multidisciplinary); Transfer learning; Deep learning; Blood vessel segmentation; Ensemble learning; Retinal disease classification; Dual-branch Input
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-04503-5

Retinal disorders, posing significant risks of the loss of vision or blindness, are increasingly prevalent, due to factors such as the aging population and chronic conditions like diabetes. Traditional diagnostic methods, relying on manually analyzing images, often have problems making an early detection and with their accuracy and efficiency, largely due to the subjectivity of human judgment and the time-consuming nature of the process. This study introduces a novel AI-based framework for diagnosing retinal disease, referred to as RetinaDNet . This framework leverages dual-branch input, incorporating both retinal images and vessel segmentation images, along with transfer learning and ensemble learning algorithms. This enhances the accuracy of the diagnoses and the stability of the model, particularly in scenarios with small sample sizes. By using vascular features and mitigating the risk of overfitting, this framework demonstrates superior performance in terms of multiple metrics. In particular, a soft voting classifier combined with the ResNet50 model attains accuracy rate of 99.2% on the diabetic retinopathy diagnosis task, and 98.8% on the retina disease classification task. The source code can be accessed at https://github.com/yu0809/Dual-branch-retinal-diseases .