Diabetic macular edema grading based on deep neural networks

Al-Bander, Baidaa; Al-Nuaimy, Waleed; Al-Taee, Majid A.; Williams, Bryan M.; Zheng, Yalin

Abstract

Diabetic Macular Edema (DME) is a major cause of vision loss in diabetes. Its early detection and treatment is therefore a vital task in management of diabetic retinopathy. In this paper, we propose a new featurelearning approach for grading the severity of DME using color retinal fundus images. An automated DME diagnosis system based on the proposed featurelearning approach is developed to help early diagnosis of the disease and thus averts (or delays) its progression. It utilizes the convolutional neural networks (CNNs) to identify and extract features of DME automatically without any kind of user intervention. The developed prototype was trained and assessed by using an existing MESSIDOR dataset of 1200 images. The obtained preliminary results showed accuracy of (88.8 %), sensitivity (74.7%) and specificity (96.5 %). These results compare favorably to state-of-the-art findings with the added benefit of an automatic feature-learning approach rather than a time-consuming handcrafted approach.

Citation

Al-Bander, B., Al-Nuaimy, W., Al-Taee, M. A., Williams, B. M., & Zheng, Y. Diabetic macular edema grading based on deep neural networks. Presented at Ophthalmic Medical Image Analysis Third International Workshop, Athens, Greece

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Ophthalmic Medical Image Analysis Third International Workshop
• Online Publication Date: Oct 11, 2016
• Publication Date: 2016
• Deposit Date: Jun 14, 2023
• Volume: 3
• Pages: 121–128
• Book Title: Proceedings of the Ophthalmic Medical Image Analysis International Workshop 3
• DOI: https://doi.org/10.17077/omia.1055
• Public URL: https://keele-repository.worktribe.com/output/456128
• Publisher URL: https://pubs.lib.uiowa.edu/omia/article/id/27641/