L Gowri, R Haris, M Sumathi, Raja S P
SASTRA Deemed to be University, Thanjavur, Tamil Nadu, India.
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Graefes Arch Clin Exp Ophthalmol. 2025 Apr;263(4):935-956. doi: 10.1007/s00417-024-06687-4. Epub 2024 Dec 16.
This work tackles the growing problem of early identification of diabetic retinopathy and diabetic macular edema. The deep neural network design utilizes multi-scale feature fusion to improve automated diagnostic accuracy. Methods This approach uses convolutional neural networks (CNN) and is designed to combine higher-level semantic inputs with low-level textural characteristics. The contextual and localized abstract representations that complement each other are combined via a unique fusion technique.
Use the MESSIDOR dataset, which comprises retinal images labeled with pathological annotations, for model training and validation to ensure robust algorithm development. The suggested model shows a 98% general precision and good performance in diabetic retinopathy. This model achieves an impressive nearly 100% exactness for diabetic macular edema, with particularly high accuracy (0.99).
Consistent performance increases the likelihood that the vision will be upheld through public screening and extensive clinical integration.
这项工作解决了糖尿病视网膜病变和糖尿病性黄斑水肿早期识别这一日益严重的问题。深度神经网络设计利用多尺度特征融合来提高自动诊断准确性。方法 这种方法使用卷积神经网络（CNN），旨在将高级语义输入与低级纹理特征相结合。相互补充的上下文和局部抽象表示通过独特的融合技术进行组合。
使用包含有病理解析标注的视网膜图像的MESSIDOR数据集进行模型训练和验证，以确保稳健的算法开发。所提出的模型在糖尿病视网膜病变方面显示出98%的总体精度和良好性能。该模型在糖尿病性黄斑水肿方面实现了近100%的惊人准确率，尤其具有高准确性（0.99）。
一致的性能增加了通过公共筛查和广泛临床整合来维持视力的可能性。
