Design of Eye Health Monitoring System Under Color Fundus Image Visual Cup Segmentation Algorithm

Abstract

In order to study the application of color fundus image (CFI) visual cup segmentation (VCS) algorithm in the design of eye health monitoring system and solve the problem that traditional image processing technology requires artificial design features, which is time-consuming, laborious, and inaccurate, deep learning (DL) algorithm is combined with residual neural network (RNN) to propose a new eye health monitoring system based on CFI VCS algorithm. Firstly, the forward propagation (FP) algorithm and back propagation (BP) algorithm of DL are theoretically derived and improved. Then, the eye health monitoring and image processing system is designed and optimized with RNN. Finally, the effect analysis VCS of the system is carried out on the real data center platform; the accuracy, sensitivity, and specificity of image processing are compared with the traditional manual processing technology and the general residual network technology. The results show that the results of RIM-ONE data set and Glaucoma Repo data set are consistent with the expert standard; in the RIM-ONE data set, the accuracy rate, sensitivity, and specificity of traditional manual processing technology are 69.68%, 71.38%, and 74.71% respectively; the accuracy, sensitivity, and specificity of common residual network technology are 81.89%, 80.56%, and 86.82% respectively; the accuracy of the system is 93.57%, the sensitivity is 87.31%, and the specificity is 90.16%. In the Glaucoma Repo data set, the accuracy, sensitivity, and specificity of conventional manual processing technology are 75.28%, 73.35%, and 82.63% respectively; the accuracy, sensitivity, and specificity of common residual network technology are 82.74%, 81.49%, and 88.82% respectively; the accuracy, sensitivity, and specificity of the system are 94.66%, 91.33%, and 94.22% respectively, which show that the system has a good effect on the segmentation of CFI, can adapt to various features of the image, has a high degree of accuracy and robustness, and provides a new way of thinking for the monitoring and management of eye health in daily life.