Paper Info
Title
Automatic Diabetic Retinopathy Screening via Cascaded Framework Based on Image- and Lesion-Level Features Fusion.
Abstract
The early detection of diabetic retinopathy is crucial for preventing blindness. However, it is time-consuming to analyze fundus images manually, especially considering the increasing amount of medical images. In this paper, we propose an automatic diabetic retinopathy screening method using color fundus images. Our approach consists of three main components: edge-guided candidate microaneurysms detection, candidates classification using mixed features, and diabetic retinopathy prediction using fused features of image level and lesion level. We divide a screening task into two sub-classification tasks: 1) verifying candidate microaneurysms by a naive Bayes classifier; 2) predicting diabetic retinopathy using a support vector machine classifier. Our approach can effectively alleviate the imbalanced class distribution problem. We evaluate our method on two public databases: Lariboisìere and Messidor, resulting in an area under the curve of 0.908 on Lariboisìere and 0.832 on Messidor. These scores demonstrate the advantages of our approach over the existing methods.
Year
DOI
Venue
2019
10.1007/s11390-019-1977-x
Journal of Computer Science and Technology
Keywords
Field
DocType
diabetic retinopathy, feature fusion, microaneurysm, naive Bayes, support vector machine
Diabetic retinopathy,Early detection,Naive Bayes classifier,Pattern recognition,Support vector machine classifier,Computer science,Fundus (eye),Real-time computing,Artificial intelligence,Blindness,Diabetic retinopathy screening
Journal
Volume
Issue
ISSN
34
6
1000-9000
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
chengzhang zhu1153.91
Rong Hu200.34
Beiji Zou323141.61
Rongchang Zhao493.81
Changlong Chen5484.11
Yalong Xiao600.34