A Survey on Microaneurysms Detection in Color Fundus Images

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TitreA Survey on Microaneurysms Detection in Color Fundus Images
Type de publicationConference Paper
Year of Publication2020
AuteursSiswadi AAnnassia P, Bricq S, Meriaudeau F
Conference NamePROCEEDINGS OF ICORIS 2020: 2020 THE 2ND INTERNATIONAL CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEM (ICORIS)
PublisherIEEE Indonesia Sect; Univ Klabat, Comp Sci Fac; BINUS Univ; Inst Teknologi & Bisnis, STIKOM Bali
Conference Location345 E 47TH ST, NEW YORK, NY 10017 USA
ISBN Number978-1-7281-7257-6
Mots-clésClassification, Deep learning, detection, Machine learning, microaneurysm, performance, seg-mentation
Résumé

Early Detection of Microaneurysms (MA) plays a vital role in preventing the blindness caused by diabetic retinopathy (DR). DR is preventable yet a serious diabetic problem. Treatment at an earlier stage reduces the risk of blindness. Microaneurysm is the first sign of DR found in fundus images while doing screening. Detection of MA is a challenging task mainly because of its size. MA appears as a tiny red spot ranging from 15 mu m to 60 mu m size. The most common way to detect the MA from a colour fundus image is by classification/segmentation through machine learning and deep learning approaches. The FROC-based performance evaluation shows that the existing methods can reach only up to 80% of sensitivity at 8 False Positive per Image on average. In recent researches, machine learning and deep learning approaches are equally competing each other to be better in detecting MA both in lesion level as well as pixel-level.

DOI10.1109/ICORIS50180.2020.9320818