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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2013 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2013 |
| Publisher | IEEE Computer Society |
| Pages | 376-380 |
| Number of pages | 5 |
| ISBN (Print) | 9781479915088 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | 2013 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2013 - Penang, Malaysia Duration: 29 Nov 2013 → 1 Dec 2013 |
Publication series
| Name | Proceedings - 2013 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2013 |
|---|
Conference
| Conference | 2013 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2013 |
|---|---|
| Country/Territory | Malaysia |
| City | Penang |
| Period | 29/11/13 → 1/12/13 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Retinal fundus images
- classification
- hard exudates
- non-proliferative diabetic retinopathy
- soft margin SVM
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