A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy

Diabetic Retinopathy (DR) is one of the principal sources of blindness due to diabetes mellitus. It can be identified by lesions of the retina, namely microaneurysms, hemorrhages, and exudates. DR can be effectively prevented or delayed if discovered early enough and well-managed. Prior studies on diabetic retinopathy typically extract features manually but are time-consuming and not accurate. In this research, we propose a research framework using advanced retina image processing, deep learning, and a boosting algorithm for high-performance DR grading. First, we preprocess the retina image datasets to highlight signs of DR, then follow by a convolutional neural network to extract features of retina images, and finally apply a boosting tree algorithm to make a prediction based on extracted features. Experimental results show that our pipeline has excellent performance when grading diabetic retinopathy images, as evidenced by scores for both the Kaggle dataset and the IDRiD dataset.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2018

Author dc:creator

• Wang, Yu

Chair dc:contributor.committeechair

• Fan, Weiguo Patrick

Committee members dc:contributor.committeemember

• Fox, Edward A.
• Reddy, Chandan K.

• Retina Image
• Image Grading
• Diabetic Retinopathy
• Early Detection
• Feature Extraction
• ConvNN
• Deep learning (Machine learning)
• Boosting Decision Tree

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:15777

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/83607