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. / Master of Science / Diabetes is a disease in which insulin can not work very well, that leads to long-term high blood sugar level. Diabetic Retinopathy (DR), a result of diabetes mellitus, is one of the leading causes of blindness. It can be identified by lesions on the surface of the retina. DR can be effectively prevented or delayed if discovered early enough and well-managed. Prior image processing studies of diabetic retinopathy typically detect features manually, like retinal lesions, but are time-consuming and not accurate. In this research, we propose a framework using advanced retina image processing, deep learning, and a boosting decision tree algorithm for high-performance DR grading. Deep learning is a method that can be used to extract features of the image. A boosting decision tree is a method widely used in classification tasks. We preprocess the retina image datasets to highlight signs of DR, followed by deep learning to extract features of retina images. Then, we apply a boosting decision tree algorithm to make a prediction based on extracted features. The results of experiments show that our pipeline has excellent performance when grading the diabetic retinopathy score for both Kaggle and IDRiD datasets.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/83607 |
| Date | 21 June 2018 |
| Creators | Wang, Yu |
| Contributors | Computer Science, Fan, Weiguo Patrick, Fox, Edward A., Reddy, Chandan K. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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