title: Ensemble selection for feature-based classification of diabetic maculopathy images.
abstract: 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 naïve Bayes, naïve 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.
authors.affiliation: Department of Computer Science, Louisiana Tech University, Ruston, LA 71272, USA.
authors.forename: Pradeep
authors.initials: P
authors.lastname: Chowriappa
authors.forename: Sumeet
authors.initials: S
authors.lastname: Dua
authors.forename: U
authors.initials: U
authors.lastname: Rajendra Acharya
authors.forename: M
authors.initials: M
authors.lastname: Muthu Rama Krishnan
mesh_terms: D003930:Diabetic Retinopathy / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D005654:Fundus Oculi; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008269:Macular Edema / Q000145:classification / Q000175:diagnosis / Q000473:pathology; D008297:Male
keywords: Decision system; Diabetic retinopathy; Ensemble classifier; Feature extraction; Fundus imaging; Image texture