Book chapter
A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy
Smart Health, pp.240-248
Lecture Notes in Computer Science, Springer International Publishing
10/26/2018
DOI: 10.1007/978-3-030-03649-2_24
Abstract
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 image processing 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 boosting algorithm for high-performance DR grading. First, we preprocess the retina image datasets to highlight signs of DR, then employ a convolutional neural network to extract features of retina images, and finally apply a boosting tree algorithm to make a prediction. Experimental results show that our pipeline has excellent performance when grading diabetic retinopathy score on Kaggle dataset.
Details
- Title: Subtitle
- A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy
- Creators
- Yu Wang - Virginia TechG. Alan Wang - Virginia TechWeiguo Fan - Virginia TechJiexun Li - Western Washington University
- Resource Type
- Book chapter
- Publication Details
- Smart Health, pp.240-248
- Publisher
- Springer International Publishing; Cham
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-030-03649-2_24
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 10/26/2018
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380377402771
Metrics
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