Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLuis Bastos Frazaoen_US
dc.contributor.authorNipon Theera-Umponen_US
dc.contributor.authorSansanee Auephanwiriyakulen_US
dc.description.abstract© 2018 Elsevier Inc. In this paper, eye fundus images are analyzed for the automatic detection of diabetic retinopathy. One thousand two hundred eye fundus images of the Messidor database were used to test the system using the cross validation in various settings. Two types of features were extracted including the holistic texture features and the local retinal features. Four classifiers were implemented including the k-nearest neighbors, neural networks, support vector machines, and random decision forests. The best results from the analysis of holistic texture features were obtained for the Independent Component Analysis method, which had never been tested before in this type of image. Furthermore, the performance of our system improved greatly when two local retinal features — micro-aneurysms and exudates — were incorporated into the analysis, a methodology inspired by a modular approach originally developed for face-recognition tasks. The diagnostic performance of our algorithm is very promising and similar to previous automatic systems and human expert analysis on the same dataset. This framework has the potential to be used as an aiding tool for the diagnosis of diabetic retinopathy.en_US
dc.subjectComputer Scienceen_US
dc.subjectDecision Sciencesen_US
dc.titleDiagnosis of diabetic retinopathy based on holistic texture and local retinal featuresen_US
article.title.sourcetitleInformation Sciencesen_US
article.volume475en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.

Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.