Name
Abstract | ||
|---|---|---|
Diabetic Retinopathy (DR) stage classification has been regarded as a critical step for evaluation and management of diabetes retinopathy. Because of damages of the retina blood vessels caused by the high blood glucose level, different extent of microstructures, such as micro-anuerysms, hard exudates, and neovascularization, could occupy the retina area. Deep learning based Convolutional Neural Network (CNN) has recently been proved a promising approach in biomedical image analysis. In this work, representative Diabetic Retinopathy (DR) images have been aggregated into five categories according to the expertise of ophthalmologist. A group of deep Convolutional Neural Network methods have been employed for DR stage classification. State-of-the-art accuracy result has been achieved by InceptionNet V3, which demonstrates the effectiveness of utilizing deep Convolutional Neural Networks for DR image recognition. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IRI.2018.00074 | 2018 IEEE International Conference on Information Reuse and Integration (IRI) |
Keywords | Field | DocType |
diabetic retinopathy,image classification,deep convolutional neural network | Diabetic retinopathy,Retinopathy,Hard exudates,Pattern recognition,Convolutional neural network,Computer science,Artificial intelligence,Deep learning,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-2660-3 | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoliang Wang | 1 | 91 | 24.74 |
| Yongjin Lu | 2 | 1 | 2.75 |
| Yujuan Wang | 3 | 246 | 11.57 |
| Wei-bang Chen | 4 | 0 | 0.34 |