Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/13145
Title: Detection of lesions and classification of diabetic retinopathy using fundus images
Authors: Paing M.P.
Choomchuay S.
Rapeeporn Yodprom M.D.
Keywords: Biomedical engineering
Blood vessels
Deep neural networks
Image classification
Neural networks
Ophthalmology
Classification accuracy
Diabetic retinopathy
exudates
Fundus image
It supports
Local database
Microaneurysms
Retinal disease
Eye protection
Issue Date: 2017
Abstract: Diabetes retinopathy is a retinal disease that is affected by diabetes on the eyes. The main risk of the disease can lead to blindness. Detection the disease at early stage can rescue the patients from loss of vision. The major purpose of this paper is to automatically detect as well as to classify the severity of diabetic retinopathy. At first, the lesions on the retina especially blood vessels, exudates and microaneurysms are extracted. Features such as area, perimeter and count from these lesions are used to classify the stages of the disease by applying artificial neural network (ANN). We used 214 fundus images from DIARECTDB1 and local databases. We found that the system can give the classification accuracy of 96% and it supports a great help to ophthalmologists. © 2016 IEEE.
URI: https://ir.swu.ac.th/jspui/handle/123456789/13145
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015876209&doi=10.1109%2fBMEiCON.2016.7859642&partnerID=40&md5=097fdd8206d1e37618d76ada2de563d7
Appears in Collections:Scopus 1983-2021

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