C4.5: programs for machine learning
C4.5: programs for machine learning
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractal dimension estimation for texture images: a parallel approach
Pattern Recognition Letters
Digital Image Processing
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Getting the Most Out of Ensemble Selection
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
A Sorting System for Hierarchical Grading of Diabetic Fundus Images: A Preliminary Study
IEEE Transactions on Information Technology in Biomedicine
Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features
IEEE Transactions on Information Technology in Biomedicine
Wavelet-Based Energy Features for Glaucomatous Image Classification
IEEE Transactions on Information Technology in Biomedicine
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As diabetic maculopathy (DM) is a prevalent cause of blindness in the world, it is increasingly important to use automated techniques for the early detection of the disease. In this paper, we propose a decision system to classify DM fundus images into normal, clinically significant macular edema (CMSE), and non-clinically significant macular edema (non-CMSE) classes. The objective of the proposed decision system is three fold namely, to automatically extract textural features (both region specific and global), to effectively choose subset of discriminatory features, and to classify DM fundus images to their corresponding class of disease severity. The system uses a gamut of textural features and an ensemble classifier derived from four popular classifiers such as the hidden naive Bayes, naive Bayes, sequential minimal optimization (SMO), and the tree-based J48 classifiers. We achieved an average classification accuracy of 96.7% using five-fold cross validation.