Automatic Feature Learning Method for Detection of Retinal Landmarks



Al-Bander, Baidaa, Al-Nuaimy, Waleed ORCID: 0000-0001-8927-2368, Al-Taee, Majid A ORCID: 0000-0002-3252-3637, Al-Ataby, Ali and Zheng, Yalin ORCID: 0000-0002-7873-0922
(2016) Automatic Feature Learning Method for Detection of Retinal Landmarks. In: 2016 9th International Conference on Developments in eSystems Engineering (DeSE), 2016-8-31 - 2016-9-2.

[thumbnail of Auto_Feat_Lear.pdf] Text
Auto_Feat_Lear.pdf - Author Accepted Manuscript

Download (1MB)

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.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Automatic feature learning, convolutional neural network, deep learning, retinal landmarks, automated grading
Depositing User: Symplectic Admin
Date Deposited: 19 May 2017 14:53
Last Modified: 06 Jun 2024 17:55
DOI: 10.1109/DeSE.2016.4
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3007558