Computer Assisted Grade Diagnosis Using Feature Fusion and Network Algorithm Flow

Abstract

Diabetic retinopathy is one of the important causes of visual impairment and blindness. In order to strengthen the early prevention and treatment of diabetic retinal diseases and prevent the occurrence of serious diseases, feature fusion diagnosis model that combines traditional features and depth features was proposed. In the diagnostic model, four kinds of traditional algorithms were used to extract imaging features, and VGG16 and VGG19 networks were used to extract deep features. Then k-nearest neighbor, random forest, support vector machine and extreme learning machine were used to grade the degree of diabetic retinopathy based on the fused feature vectors. The diagnostic model adopted the depth feature extracted by VGG19 network and the traditional feature fusion method to achieve the best grade effect on the support vector machine, with the accuracy of 84.44%, which was better than comparative feature fusion and classifier combination methods, and also performed well in the precision rate and recall rate. The diagnostic model based on the fusion of traditional features and depth features provides a new detection method for the grading prediction of diabetic retinopathy, and at the same time improves the accuracy of clinical diagnosis.