Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Development of Computer-Aided Diagnosis System using Fundus Images
VSMM '01 Proceedings of the Seventh International Conference on Virtual Systems and Multimedia (VSMM'01)
Higher order spectra invariants for shape pattern recognition
Higher order spectra invariants for shape pattern recognition
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
Half-Against-Half multi-class support vector machines
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Pattern recognition using invariants defined from higher order spectra: 2-D image inputs
IEEE Transactions on Image Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Automated Diagnosis of Glaucoma Using Digital Fundus Images
Journal of Medical Systems
Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review
Journal of Medical Systems
An Integrated Index for the Identification of Diabetic Retinopathy Stages Using Texture Parameters
Journal of Medical Systems
A systematic approach to embedded biomedical decision making
Computer Methods and Programs in Biomedicine
Computer-aided diagnosis of diabetic retinopathy: A review
Computers in Biology and Medicine
Detection and classification of retinal lesions for grading of diabetic retinopathy
Computers in Biology and Medicine
Intelligent Data Analysis
Automated detection of exudates and macula for grading of diabetic macular edema
Computer Methods and Programs in Biomedicine
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Diabetic retinopathy (DR) is a condition where the retina is damaged due to fluid leaking from the blood vessels into the retina. In extreme cases, the patient will become blind. Therefore, early detection of diabetic retinopathy is crucial to prevent blindness. Various image processing techniques have been used to identify the different stages of diabetes retinopathy. The application of non-linear features of the higher-order spectra (HOS) was found to be efficient as it is more suitable for the detection of shapes. The aim of this work is to automatically identify the normal, mild DR, moderate DR, severe DR and prolific DR. The parameters are extracted from the raw images using the HOS techniques and fed to the support vector machine (SVM) classifier. This paper presents classification of five kinds of eye classes using SVM classifier. Our protocol uses, 300 subjects consisting of five different kinds of eye disease conditions. We demonstrate a sensitivity of 82% for the classifier with the specificity of 88%.