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 pap...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:Lariboisière and Messidor,resulting in an area under the curve of 0.908 on Lariboisière and 0.832 on Messidor.These scores demonstrate the advantages of our approach over the existing methods.展开更多
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.61573380 and 61702559the Planned Science and Technology Project of Hunan Province of China under Grant No.2017WK2074the Natural Science Foundation of Hunan Province of China under Grant No.2018JJ3686。
文摘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:Lariboisière and Messidor,resulting in an area under the curve of 0.908 on Lariboisière and 0.832 on Messidor.These scores demonstrate the advantages of our approach over the existing methods.
