Neural Networks
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Digital Image Processing
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification and Localisation of Diabetic-Related Eye Disease
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Comparative Exudate Classification Using Support Vector Machines and Neural Networks
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
A Model-Based Approach for Automated Feature Extraction in Fundus Images
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Top-Down and Bottom-Up Strategies in Lesion Detection of Background Diabetic Retinopathy
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence)
A simple method for fitting of bounding rectangle to closed regions
Pattern Recognition
Information Sciences: an International Journal
Identification of different stages of diabetic retinopathy using retinal optical images
Information Sciences: an International Journal
Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images
Journal of Medical Systems
Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages
Journal of Medical Systems
Automated Diagnosis of Glaucoma Using Digital Fundus Images
Journal of Medical Systems
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy
Journal of Medical Systems
Small retinal vessels extraction towards proliferative diabetic retinopathy screening
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A novel optic disc detection scheme on retinal images
Expert Systems with Applications: An International Journal
A Sorting System for Hierarchical Grading of Diabetic Fundus Images: A Preliminary Study
IEEE Transactions on Information Technology in Biomedicine
Morphological grayscale reconstruction in image analysis: applications and efficient algorithms
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features
IEEE Transactions on Information Technology in Biomedicine
Computer-aided diagnosis of diabetic retinopathy: A review
Computers in Biology and Medicine
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Human eye is one of the most sophisticated organ, with retina, pupil, iris cornea, lens and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. DR is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of DR are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources used for mass screening of DR. We present an automatic screening system for the detection of normal and DR stages (NPDR and PDR). The proposed systems involves processing of fundus images for extraction of abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture and entropies. Our protocol uses total of 156 subjects consisting of two stages of DR and normal. In this work, we have fed thirteen statistically significant (p