Design Of Portable Glaucoma Screening System Based On Embedded System

Glaucoma is one of the leading causes of irreversible blindness.The disease is usually asymptomatic in its early stages,and many patients are not diagnosed until later stages,leading to delayed treatment.The existing glaucoma screening program needs a lot of manpower and material resources,and it is difficult to carry out largescale screening work.To solve this problem,this paper designs a portable glaucoma screening system based on embedded technology,and combines deep learning algorithm with embedded technology organically to realize the portable automatic screening function of glaucoma.The specific research contents are as follows:First of all,combined with the characteristics and needs of glaucoma screening,the function of the system and related technologies were analyzed,and the two main functions of the system--glaucoma screening function and follow-up and monitoring function were determined,and the technical scheme was developed.Secondly,a glaucoma screening algorithm based on fundus images was proposed.A network model called Trap Net was designed,which was composed of several multi-branch network blocks with decreasing sensitivity fields.On the one hand,this structure could extract multi-scale features to the maximum extent to improve the utilization rate of features,on the other hand,it could avoid the loss of detail features caused by the deepening of network layers.In order to improve the reasoning performance of the algorithm on embedded devices,the structure reparameterization algorithm was extended for Trap Net.The expanded structure reparameterization algorithm was used to decouple the training and reasoning of the model,and the parameters of the multi-branch training model after the training were equivalent transformed into a single way reasoning model.The superiority of the algorithm is verified by comparing the Trap Net model with OIA-ODIR data set.Finally,the hardware selection and software implementation of the portable fundus camera system are completed.The model is deployed on the portable fundus camera system through the MNN inference platform,realizing the function of glaucoma screening.A backend service system based on the microservice architecture is built and deployed on a cloud server using Docker technology.The patient’s condition is quantified into cup-disc ratio data by leveraging cloud computing technology in the backend service system,achieving follow-up monitoring functionality.