The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Comparative Study on Diabetic Retinopathy Detection Using Texture-Based Feature Extraction Techniques
|
|
Author(s): Azam Asilian Bidgoli (University of Kashan, Iran), Hossein Ebrahimpour-Komleh (University of Kashan, Iran)and Seyed Jalaleddin Mousavirad (University of Kashan, Iran)
Copyright: 2017
Pages: 30
Source title:
Computational Tools and Techniques for Biomedical Signal Processing
Source Author(s)/Editor(s): Butta Singh (Guru Nanak Dev University, India)
DOI: 10.4018/978-1-5225-0660-7.ch012
PurchaseView on the publisher's website for pricing and purchasing information.
|
Abstract
Diabetic retinopathy is proved to be one of the most important eye disorders in recent decades that late diagnosis of it may cause low vision or even blindness. Specialist are able to detect retinopathy in retinal images using machine learning as a decision support system which helps accelerate and facilitate the diagnosis. The automated diabetic retinopathy is a difficult computer vision problem –with the goal of detecting features of retinopathy. The present chapter is written with the purpose of analyzing and comparing different feature extraction methods to evaluate the best algorithm for detection retinopathy with least error. Extracted features using these methods are used to classify images into normal and altered groups.
Related Content
|
Aswathy Ravikumar, Harini Sriraman.
© 2023.
18 pages.
|
|
Ezhilarasie R., Aishwarya N., Subramani V., Umamakeswari A..
© 2023.
10 pages.
|
|
Sangeetha J..
© 2023.
13 pages.
|
|
Manivannan Doraipandian, Sriram J., Yathishan D., Palanivel S..
© 2023.
14 pages.
|
|
T. Kavitha, Malini S., Senbagavalli G..
© 2023.
36 pages.
|
|
Uma K. V., Aakash V., Deisy C..
© 2023.
23 pages.
|
|
Alageswaran Ramaiah, Arun K. S., Yathishan D., Sriram J., Palanivel S..
© 2023.
17 pages.
|
|
|