Classification And Description Of Fundus Images Based On ResNet

Retinopathy caused by diabetes,because its early symptoms are not significant,can not be cured in the late stage,has been a difficult problem for ophthalmology.Clinical practice has proven that early detection of such diseases is of great significance for the control and treatment of diseases.Researchers in different fields have carried out related research work in this field from different angles.Among them,the application of artificial intelligence and machine learning technology to the early detection of fundus lesions is an important research direction in this field.In recent years,deep learning with neural network as the core has developed rapidly,especially in the field of image and speech recognition,and has been successfully applied in the field of medical image diagnosis.In this paper,based on Residual Networks(ResNets)retinal retinopathy image recognition and automatic description of the disease,the main research contents are as follows:1)Carry out research on sample generation and image enhancement methods to overcome the problems of insufficient sample and uneven distribution,thus effectively improving the learning quality of neural networks.2)A classification method based on ResNet(residual neural network)for fundus lesion images is proposed.For the convolutional neural network,the learning rate increases or decreases with the depth during the training process.The residual neural network replaces the traditional one.The convolutional neural network solves the degradation problem through residual learning,and modifies the last layer of the residual network to classify the lesions.3)A method for labeling and understanding the fundus lesion image based on ResNet-attLSTM model is proposed.The model is divided into two layers,the bottom layer is ResNet,which is used for image feature extraction.The upper layer is attLSTM,which processes the features of ResNet output,and finally outputs the description information about the image.