The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Convolutional Neural Networks for Diabetic Retinopathy
Open accessAbstract
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.
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Cites
Deep Residual Learning for Image Recognition
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
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Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.
Gardner, G. G., Keating, D., Williamson, T. H., & Elliott, A. T. (1996) · British Journal of Ophthalmology · doi:10.1136/bjo.80.11.940 · Cited by 508
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Medical image analysis using convolutional neural networks: a review
Anwar, S. M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., & Khan, M. K. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1088-1 · Cited by 1,431
Deep Learning for Medical Image Processing: Overview, Challenges and the Future
Razzak, M. I., Naz, S., & Zaib, A. (2018) · Lecture Notes in Computational Vision and Biomechanics · doi:10.1007/978-3-319-65981-7_12 · Cited by 1,248
Convolutional neural networks in medical image understanding: a survey
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Diabetic retinopathy detection through deep learning techniques: A review
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Metadata
- Authors
- Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2016
- Volume
- 90
- Pages
- 200-205
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2502390809
- Cited by
- 988
- References
- 19
- FWCI
- 101.25
- Type
- conference paper
- Language
- en
- Open access
- diamond
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