Publication: Detection of lesions and classification of diabetic retinopathy using fundus images
0
0
Issued Date
2017
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-85015876209
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
BMEiCON 2016 - 9th Biomedical Engineering International Conference. (2017)
Suggested Citation
Paing M.P., Choomchuay S., Rapeeporn Yodprom M.D. Detection of lesions and classification of diabetic retinopathy using fundus images. BMEiCON 2016 - 9th Biomedical Engineering International Conference. (2017). doi:10.1109/BMEiCON.2016.7859642 Retrieved from: https://hdl.handle.net/20.500.14740/4247
Author(s)
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.