基于注意力神经网络的糖尿病视网膜病变识别

Recognition of diabetic retinopathy based on attention neural network

针对目前糖尿病视网膜病变识别主要依赖于医生的临床经验,病变特征难以用肉眼区分且识别率较低等问题,提出一种基于注意力神经网络的糖尿病视网膜病变分类方法。首先,对原始数据集中的视网膜图像进行归一化、直方图均衡化和数据增强等预处理;其次,调整经典的DenseNet,在避免梯度消失和保证分类精度的前提下,有针对性地减少连接数,提出了2-DenseNet,同时将注意力模块嵌入到2-DenseNet中,指导网络关注视网膜图像中的渗出物、厚血管和微动脉瘤等特征,使用改进后的网络对预处理后的图像进行训练并测试;最后,在公开的Kaggle数据集上对多个网络进行对比,实验结果表明,该网络对糖尿病视网膜病变的分类性能高于其他对比网络。

To solve the problems that the identification of diabetic retinopathy mainly depends on the clinical experience of doctors and the features of the lesions are difficult to be distinguished by eyes and the recognition rate is low,a diabetic retinopathy classification method based on attention neural network is proposed.Firstly,the retinal images are preprocessed by normalization,histogram equalization and data enhancement.Secondly,2-DenseNet is proposed by adjusting the classical DenseNet to reduce the number of connections on the premise of avoiding gradient disappearance and ensuring classification accuracy.At the same time,the attention module is embedded into the network to direct it to focus on features such as exudates,thick blood vessels,and microaneurysms in retinal images,which is used to train and test the pre-processed images.Finally,multiple models are compared on the public Kaggle dataset,and the experimental results show that the network has a higher classification accuracy for diabetic retinopathy than other models.