Automatic Feature Learning Method for Detection of Retinal Landmarks

Al-Bander, Baidaa; Al-Nuaimy, Waleed; Al-Taee, Majid A.; Al-Ataby, Ali; Zheng, Yalin

Abstract

This paper presents an automatic deep learning method for location detection of important retinal landmarks, the fovea and optic disc (OD) in digital fundus retinal images with the potential for use in an automated screening and grading system. The proposed method is based on deep convolutional neural networks (CNN) and does not depend the visual appearance or anatomical features of the retinal landmarks. It comprises convolution, max-pooling, fully connected and dropout layers as well as an output layer. The CNN is trained using an existing dataset images along with their annotated locations of the foveal and OD centres. Performance of the network is evaluated using Root Mean Square Error (RMSE). The developed feature learning-based approach presents promising system for retinal landmarks detection.

Citation

Al-Bander, B., Al-Nuaimy, W., Al-Taee, M. A., Al-Ataby, A., & Zheng, Y. (2016, August). Automatic Feature Learning Method for Detection of Retinal Landmarks. Presented at 2016 9th International Conference on Developments in eSystems Engineering (DeSE), Liverpool, United Kingdom

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2016 9th International Conference on Developments in eSystems Engineering (DeSE)
• Start Date: Aug 31, 2016
• End Date: Sep 2, 2016
• Online Publication Date: May 18, 2017
• Publication Date: 2017
• Deposit Date: Jun 14, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• DOI: https://doi.org/10.1109/DeSE.2016.4
• Public URL: https://keele-repository.worktribe.com/output/456867