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Cited by 988

The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.

Convolutional Neural Networks for Diabetic Retinopathy

Open access
Pratt, Harry, Coenen, Frans, Broadbent, Deborah M., Harding, Simon P., Zheng, Yalin·2016·Procedia Computer Science
DOI 10.1016/j.procs.2016.07.014
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Abstract

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 set of 80,000 images used our proposed CNN achieves a sensitivity of 95% and an accuracy of 75% on 5,000 validation images.

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Metadata

Authors
Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y.
Venue
Procedia Computer Science
Publisher
Elsevier BV
Published
2016
Volume
90
Pages
200-205
ISSN
1877-0509 (ISSN-L)
DOI
10.1016/j.procs.2016.07.014
OpenAlex
W2502390809
Cited by
988
References
19
FWCI
101.25
Type
conference paper
Language
en
Open access
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