title: Machine learning and pattern classification in identification of indigenous retinal pathology.
abstract: Diabetic retinopathy (DR) is a complication of diabetes, which if untreated leads to blindness. DR early diagnosis and treatment improve outcomes. Automated assessment of single lesions associated with DR has been investigated for sometime. To improve on classification, especially across different ethnic groups, we present an approach using points-of-interest and visual dictionary that contains important features required to identify retinal pathology. Variation in images of the human retina with respect to differences in pigmentation and presence of diverse lesions can be analyzed without the necessity of preprocessing and utilizing different training sets to account for ethnic differences for instance.
authors.affiliation: Centre for Research in Complex Systems, School of Community Health, Albury, NSW, Australia. hjelinek@csu.edu.au
authors.forename: Herbert F
authors.initials: HF
authors.lastname: Jelinek
authors.forename: Anderson
authors.initials: A
authors.lastname: Rocha
authors.forename: Tiago
authors.initials: T
authors.lastname: Carvalho
authors.forename: Siome
authors.initials: S
authors.lastname: Goldenstein
authors.forename: Jacques
authors.initials: J
authors.lastname: Wainer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003930:Diabetic Retinopathy / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity