Classification of diabetic retinopathy using neural networks

Author

Nguyen, H.T. ; Butler, M. ; Roychoudhry, A. ; Shannon, A.G. ; Flack, J. ; Mitchell, P.

Author_Institution

Sch. of Electr. Eng., Univ. of Technol., Sydney, NSW, Australia

Volume

4

fYear

1996

fDate

31 Oct-3 Nov 1996

Firstpage

1548

Abstract

Classification of the severity of diabetic retinopathy (DR) and quantification of diabetic changes are vital for assessing the therapies and risk factors for this frequent complication of diabetes. A multilayer feedforward network has been developed for the classification of DR. One of its major strengths is that accurate feature extractions and accurate grading of DR lesions are not required. Another strength of this technique is its robustness as the network can also classify DR effectively in noisy environments

Keywords

backpropagation; eye; feature extraction; feedforward neural nets; image classification; medical image processing; automated grading; classification; cropped image; error backpropagation; multilayer feedforward neural network; noisy environments; quantification of diabetic changes; risk factors; robustness; severity of diabetic retinopathy; visual loss; Australia; Diabetes; Engineering in medicine and biology; Hospitals; Lesions; Medical treatment; Neural networks; Retina; Retinopathy; Visual perception;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE

Conference_Location

Amsterdam

Print_ISBN

0-7803-3811-1

Type

conf

DOI

10.1109/IEMBS.1996.647546

Filename

647546