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<p>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 <b>RetinaDNet</b>. 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 <a href="https://github.com/yu0809/Dual-branch-retinal-diseases">https://github.com/yu0809/Dual-branch-retinal-diseases</a>.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月2日

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  • Hongjie Yu,
  • Xingbo Dong

摘要

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.