Research And Implementation On Diabetic Retinopathy Recognition Algorithm Based On CNN

In recent years,the development of artificial intelligence technology has become more rapid,and its application in various industries has also increased.Convolutional neural network and deep learning are import algorithms in the filed of artificial intelligence.With the continuous improvement of people's living standards,the lack of conventional sports and the increase in the age of aging,the incidence of diabetes in the world is increasing year by year.Retinal image information can be used as a basis for diagnosing diabetes.However,the traditional method based on doctor's human eye recognition is inefficient and the diagnosis result is error-prone.Therefore,how to classify retinal images quickly and efficiently becomes one of the problerms that need to be solved urgently.Firstly,this paper analyzes the diabetic retinal image data set,and proposes four preprocessing methods for the problems of retinal image,so that the processed retinal images can be used as the training set,test set and verification set of the model.Then,based on the classification of retinal images by ophthalmologists and the traditional image classification algorithm,this paper studies the retinal image classification algorithm based on convolutional neural network.According to the characteristics of ResNet and DenseNet network structure,this paper designs a new convolutional neural network model.At the same time,the early stop method and the addition of residual blocks were used to prevent overfitting and the disappearance of gradients.Finally,this paper tunes model parameters so that the classification accuracy of retinal images is more than 95%.This paper tests the retina image recognition effect of the model on the Kaggle dataset and the messidor-2 dataset,and calculates the sensitivity,specificity,F1 score and AUC value.Experimental results show that the improved neural network model has good recognition accuracy and generalization ability.According to the designed model,this paper develops a diabetic retinal image detection system.The system uses a convolutional neural network-based retinal image classification method to make full use of the self-learning advantages of deep learning.It can classify retinal images quickly and accurately and provide a basis for doctor to diagnose patients,then it can perform statistics and calculations and reduce doctor's operations.The system can save the retinal image at each diagnosis and provide a data set for subsequent retinal image classification studies.