Deep Learning Based Hypertension Prediciton On Retinal Fundus Image

Retinal fundus images are an important reference for ophthalmologists to obtain patients’ fundus diseases(such as glaucoma,pathological myopia,cataract)and other chronic diseases(such as diabetes,hypertension,age-related macular degeneration,etc.).Among them,hypertension is the most common chronic disease and the most important risk factor for cardiovascular and cerebrovascular diseases.Through the analysis of retinal fundus images,ophthalmologists can predict whether there is a hypertensive disease,and then propose effective prevention and control measures.Based on deep learning technology,this paper assists ophthalmologists in predicting hypertensive diseases in fundus images.The main tasks of the paper are as follows:1.Hypertension disease prediction and fundus image data argumentation based on generative adversarial network.Due to the unbalanced distribution of positive and negative samples in medical images,in order to increase the number of samples in the case of limited labeled data,consider using generative adversarial networks for fundus image data argumentation,and introduce an adaptive style transfer module into the generator,a grouped channel attention module is introduced into the discriminator,both significantly improve the quality and diversity of fundus image generation,and enables model training on a larger scale,2.A Semantic Segmentation Model Based on Attention Mechanism.In the image segmentation model,in view of the discontinuity of arterial and venous blood vessel segmentation and the misclassification of blood vessel boundary blurred regions,a cross attention module is proposed to solve the problems of blood vessel segmentation consistency and border blurring.Fusion on encoder and decoder features and regional segmentation loss function are introduced to improve the accuracy of arterial and vein segmentation,3.A method for predicting hypertensive disease in fundus images with semantic priors.Combined with the analysis of fundus images by ophthalmologists,the segmentation mask of arterial and venous vessels was introduced as the prior information of hypertension prediction in fundus images,and the connection between the image segmentation model and the image classification model is established.In the fundus image classification model,the methods of image fusion and feature fusion are compared and analyzed,and the segmented arterial and venous information is fully utilized to propose an image semantic fusion method to improve the prediction effect of fundus images for hypertension.