Comput. Biol. Med. - Ensemble selection for feature-based classification of diabetic maculopathy images.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
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Resumo

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 na?ve Bayes, na?ve 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.

Resumo Limpo

diabet maculopathi dm preval caus blind world increas import use autom techniqu earli detect diseas paper propos decis system classifi dm fundus imag normal clinic signific macular edema cmse nonclin signific macular edema noncms class object propos decis system three fold name automat extract textur featur region specif global effect choos subset discriminatori featur classifi dm fundus imag correspond class diseas sever system use gamut textur featur ensembl classifi deriv four popular classifi hidden nave bay nave bay sequenti minim optim smo treebas j classifi achiev averag classif accuraci use fivefold cross valid

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