2014/01/20 by Bálint Antal, Balint Antal, András Hajdú +1 · 5 citations
Computer Science · Mathematics · Medicine · #Glaucoma and retinal disorders #Retinal Diseases and Treatments #Retinal Imaging and Analysis #cs.CV #cs.LG #stat.AP #stat.ML
paper · pdf · doi:10.1016/j.knosys.2013.12.023
published as Knowledge-Based Systems, Elsevier, Volume 60, April 2014, Pages 20-27
openalex publication_date 2014/01/20 · arxiv created 2014/10/30 · arxiv updated 2014/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In this paper, an ensemble-based method for the screening of diabetic retinopathy (DR) is proposed. This approach is based on features extracted from the output of several retinal image processing algorithms, such as image-level (quality assessment, pre-screening, AM/FM), lesion-specific (microaneurysms, exudates) and anatomical (macula, optic disc) components. The actual decision about the presence of the disease is then made by an ensemble of machine learning classifiers. We have tested our approach on the publicly available Messidor database, where 90% sensitivity, 91% specificity and 90% accuracy and 0.989 AUC are achieved in a disease/no-disease setting. These results are highly competitive in this field and suggest that retinal image processing is a valid approach for automatic DR screening.