Computer-aided diagnosis of diabetic retinopathy: A review
By M. Mookiah, U. R. Acharya, Chua Kuang Chua, Choo Min Lim, E. Ng, A. Laude. Comput. Biol. Medicine, 2013
435 citations recorded by the source corpus.
Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/7323be3a97d47db48af1e56daff20487e6265b0a
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In Ophthalmology
In Radiology, Nuclear Medicine and Imaging
- Rishab Gargeya
- Theodore Leng
- Yu Qiao
- Carson K. Lam
- Margaret Guo
- Tony Lindsey
- Dale R. Webster
- Darvin Yi
Citations in the corpus
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. (2016, methodology): “Additional sensitivity analyses were conducted for several subcategories: (1) detecting moderate or worse diabetic retinopathy only; (2) detecting severe or worse diabetic retinopathy only; (3) detecting referable diabetic macular edema only; (4) image quality; and (5) referable diabetic retinopathy”
- Automatic hemorrhage detection in color fundus images based on gradual removal of vascular branches (2016, background): “During the past decade, a lot of methods aiming at detecting these lesions have been proposed [1].”
- Automated Detection of Diabetic Retinopathy using Deep Learning (2018, background): “Computer-aided diagnosis of diabetic retinopathy has been explored in the past to reduce the burden on opthamologists and mitigate diagnostic inconsistencies between manual readers 16 .”
- Convolutional Neural Networks for Diabetic Retinopathy (2016, methodology): “Significant work has been done on detecting the features of DR using automated methods such as support vector machines and k-NN classifiers 9 .”
- Automated Identification of Diabetic Retinopathy Using Deep Learning. (2017, methodology): “Indeed, methods derived from a singular data set may not generalize to fundus images obtained from other clinical studies that use different types of fundus cameras, eye dilation methods, or both, hindering clinical impact in real-world workflows.(8,9) Moreover, many of these algorithms depend on ma”