Convolutional Neural Networks for Diabetic Retinopathy

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 approac...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Procedia computer science Jg. 90; S. 200 - 205
Hauptverfasser: Pratt, Harry, Coenen, Frans, Broadbent, Deborah M., Harding, Simon P., Zheng, Yalin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 2016
Schlagworte:
Convolutional Neural Networks
Deep Learning
Diabetes
Diabetic Retinopathy
Image Classification
Image Classification
Convolutional Neural Networks
Diabetes
Deep Learning
Diabetic Retinopathy
ISSN:1877-0509, 1877-0509
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2016.07.014