Convolutional Neural Networks for Diabetic Retinopathy

Pratt, Harry, Coenen, Frans ORCID: 0000-0003-1026-6649, Broadbent, Deborah M, Harding, Simon P ORCID: 0000-0003-4676-1158 and Zheng, Yalin ORCID: 0000-0002-7873-0922
(2016) Convolutional Neural Networks for Diabetic Retinopathy. In: International Conference On Medical Imaging Understanding and Analysis 2016, MIUA 2016, 6-8 July 2016,, 2016-07-06 - 2016-07-08, Loughbrough University.

This is the latest version of this item.

[img] Text
pratt_MIUA16-1.pdf - Published Version

Download (8MB)


The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a difficult and time consuming task. In this paper, we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify the intricate features involved in the classification task such as micro-aneurysms, exudate and haemorrhages on the retina and consequently provide a diagnosis automatically and without user input. We train this network using a high-end graphics processor unit (GPU) on the publicly available Kaggle dataset and demonstrate impressive results, particularly for a high-level classification task. On the data

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Deep Learning, Convolutional Neural Networks,, Diabetic Retinopathy, Image Classification, Diabetes
Depositing User: Symplectic Admin
Date Deposited: 20 Aug 2018 13:48
Last Modified: 24 Jan 2021 19:11
DOI: 10.1016/j.procs.2016.07.014

Available Versions of this Item