Classification of non-proliferative diabetic retinopathy based on hard exudates using soft margin SVM

Author

Tjandrasa, Handayani ; Putra, Ricky Eka ; Wijaya, Arya Yudhi ; Arieshanti, Isye

Author_Institution

Sepuluh Nopember Inst. of Technol. (ITS), Surabaya, Indonesia

fYear

2013

fDate

Nov. 29 2013-Dec. 1 2013

Firstpage

376

Lastpage

380

Abstract

Diabetic retinopathy is a retinal disease caused by diabetes mellitus. Severity of diabetic retinopathy may lead to blindness. Therefore, early detection of diabetic retinopathy is very important. One of diabetic retinopathy symptoms is the existence of hard exudates. In this study, hard exudates in retinal fundus images are employed to classify the moderate and severe non-proliferative diabetic retinopathy. The hard exudates are segmented using mathematical morphology and the extracted features are classified by using soft margin SVM. The classification model achieves accuracy of 90.54% for 75 training data and 74 testing data of retinal images.

Keywords

diseases; eye; feature extraction; image classification; image segmentation; mathematical morphology; medical image processing; support vector machines; diabetes mellitus; feature extraction; hard exudate segmentation; mathematical morphology; nonproliferative diabetic retinopathy classification; retinal disease; retinal fundus images; soft margin SVM; testing data; training data; Diabetes; Feature extraction; Image segmentation; Optical imaging; Retina; Retinopathy; Support vector machines; Retinal fundus images; classification; hard exudates; non-proliferative diabetic retinopathy; soft margin SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on

Conference_Location

Mindeb

Print_ISBN

978-1-4799-1506-4

Type

conf

DOI

10.1109/ICCSCE.2013.6719993

Filename

6719993