Classification and Detection of Diabetic Retinopathy in Ultra-widefield Images Based on Attention Mechanism
To address the issue of low accuracy in image classification of Diabetic Retinopathy(DR),a classification method based on attention mechanisms is proposed.This method initially employs preprocessing techniques such as cropping,mean filtering,Gaussian filtering,and CLA-HE,The model is based on the DenseNet architecture and incorporates channel attention mecha-nisms and spatial attention mechanisms to further enhance its ability to recognize key features.Experimental results show that,compared to traditional methods,the improved model exhibits superior classification performance.On the Kaggle dataset,its average accuracy reached 89.15％,with an average specificity of 94.22％.On the ultra-wide-angle dataset,its average ac-curacy reached 91.24％,with an average specificity of 95.72％.
万方数据
维普
