Journal ArticleDOI
Detection of exudates using neuro-fuzzy technique:
J. Jeyapriya,K. S. Umadevi +1 more
TLDR
A neuro-fuzzy technique, which is developed for identification of exudates in retinal images and is utilized for the extraction of essential features from the retinal image of patient’s eyeballs, achieves an accuracy and consistency rate of 91% in correspondence with the ophthalmologist.Abstract:
Diabetic retinopathy is a common condition of an ailment where the retina is harmed on the grounds that fluid breaks away from the walls of blood vessels into the retina. The large population is af...read more
Citations
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Journal ArticleDOI
Fractal analysis of retinal vasculature in relation with retinal diseases – an machine learning approach
TL;DR: The STARE database’s forecast of the association between fractal dimensions and various retinal disorders and the E-ophtha-EX database‘s accomplishment of significance are the study”s main highlights.
References
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Journal ArticleDOI
Detection of blood vessels in retinal images using two-dimensional matched filters
TL;DR: The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images and the results are compared to those obtained with other methods.
Journal ArticleDOI
Deep image mining for diabetic retinopathy screening.
TL;DR: In this article, a generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps, showing which pixels in images play a role in the image-level predictions.
Journal ArticleDOI
Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review
TL;DR: Algorithm used for the extraction of features of diabetic retinopathy from digital fundus images, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture are reviewed.
Journal ArticleDOI
Computer-based detection of diabetes retinopathy stages using digital fundus images.
TL;DR: Morphological image processing and support vector machine (SVM) techniques were used for the automatic diagnosis of eye health and a sensitivity of more than 82 per cent and specificity of 86 per cent was demonstrated for the system developed.
Journal ArticleDOI
Transforming Retinal Photographs to Entropy Images in Deep Learning to Improve Automated Detection for Diabetic Retinopathy.
Gen-Min Lin,Mei-Juan Chen,Chia-Hung Yeh,Yu Yang Lin,Heng Yu Kuo,Min Hui Lin,Ming Chin Chen,Shinfeng D. Lin,Ying Gao,Anran Ran,Carol Y. Cheung +10 more
TL;DR: It is drawn that transformed entropy imaging of fundus photographs can increase the machinery detection accuracy, sensitivity, and specificity of referable DR for the deep learning-based system.