Design of Auxiliary Diagnosis System for Diabetic Fundus Retinopathy
Screening for diabetic retinopathy is important for preventing blindness.The comput-er-aided diagnosis system can process and analyze image data quickly and accurately,and provide objective digital diagnosis basis for doctors.However,when the resolution of pathological ima-ges is low or the difference between categories is small,the classification accuracy still needs to be improved.In this paper,a CBAM Convolutional Block Attention Module based ResNet deep learning network model is designed and applied to the screening of diabetic retinopathy.Wechat mini program was designed to facilitate users to view the test results and realize an auxiliary sys-tem for diabetic fundus retinopathy.The experimental results show that the system designed in this paper can accurately detect diabetic retinopathy with the recognition accuracy of 94.72％,sensitivity of 92.67％,specificity of 99.83％,and provide a convenient client platform for pa-tients.
NETL
NSTL
万方数据
