Zhang Lirong, Gang Jialin, Liu Jiangbo, Zhou Hui, Xiao Yao, Wang Jiaolin, Guo Yuyang
The School of Digital Art and Design, Dalian Neusoft University of Information, Dalian, Liaoning, China.
Med Biol Eng Comput. 2025 Feb;63(2):365-381. doi: 10.1007/s11517-024-03194-w. Epub 2024 Sep 25.
Diabetic retinopathy is a chronic disease of the eye that is precipitated via diabetes. As the disease progresses, the blood vessels in the retina are issue to modifications such as dilation, leakage, and new blood vessel formation. Early detection and treatment of the lesions are vital for the prevention and reduction of imaginative and prescient loss. A new dual-path multi-module network algorithm for diabetic retinopathy classification is proposed in this paper, aiming to accurately classify the diabetic retinopathy stage to facilitate early diagnosis and intervention. To obtain the purpose of fact augmentation, the algorithm first enhances retinal lesion features using color correcting and multi-scale fusion algorithms. It then optimizes the local records via a multi-path multiplexing structure with convolutional kernels of exclusive sizes. Finally, a multi-feature fusion module is used to improve the accuracy of the diabetic retinopathy classification model. Two public datasets and a real hospital dataset are used to validate the algorithm. The accuracy is 98.9%, 99.3%, and 98.3%, respectively. The experimental results not only confirm the advancement and practicability of the algorithm in the field of automatic DR diagnosis, but also foretell its broad application prospects in clinical settings, which is expected to provide strong technical support for the early screening and treatment of diabetic retinopathy.
糖尿病性视网膜病变是一种由糖尿病引发的眼部慢性疾病。随着病情发展，视网膜中的血管会出现诸如扩张、渗漏和新生血管形成等变化。病变的早期检测和治疗对于预防和减少视力丧失至关重要。本文提出了一种用于糖尿病性视网膜病变分类的新型双路径多模块网络算法，旨在准确分类糖尿病性视网膜病变阶段，以促进早期诊断和干预。为了实现数据增强的目的，该算法首先使用颜色校正和多尺度融合算法增强视网膜病变特征。然后，它通过具有不同大小卷积核的多路径复用结构优化局部数据。最后，使用多特征融合模块提高糖尿病性视网膜病变分类模型的准确性。使用两个公共数据集和一个真实医院数据集对该算法进行验证。准确率分别为98.9%、99.3%和98.3%。实验结果不仅证实了该算法在糖尿病性视网膜病变自动诊断领域的先进性和实用性，还预示了其在临床环境中的广阔应用前景，有望为糖尿病性视网膜病变的早期筛查和治疗提供强有力的技术支持。
