The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Bloodless Technique to Detect Diabetes using Soft Computational Tool
Abstract
Detection of diabetes using bloodless technique is an important research issue in the area of machine learning and artificial intelligence (AI). Here we present the working of a system designed to detect the abnormality of the eye with pain and blood free method. The typical features for diabetic retinopathy (DR) are used along with certain soft computing techniques to design such a system. The essential components of DR are blood vessels, red lesions visible as microaneurysms, hemorrhages and whitish lesions i.e., lipid exudates and cotton wool spots. The chapter reports the use of a unique feature set derived from the retinal image of the eye. The feature set is applied to a Support Vector Machine (SVM) which provides the decision regarding the state of infection of the eye. The classification ability of the proposed system for blood vessel and exudate is 91.67% and for optic disc and microaneurysm is 83.33%.
Related Content
|
Sharon L. Burton.
© 2024.
25 pages.
|
|
Laura Ann Jones, Ian McAndrew.
© 2024.
24 pages.
|
|
Olayinka Creighton-Randall.
© 2024.
14 pages.
|
|
Stacey L. Morin.
© 2024.
11 pages.
|
|
N. Nagashri, L. Archana, Ramya Raghavan.
© 2024.
22 pages.
|
|
Esther Gani, Foluso Ayeni, Victor Mbarika, Abdullahi I. Musa, Oneurine Ngwa.
© 2024.
25 pages.
|
|
Sia Gholami, Marwan Omar.
© 2024.
18 pages.
|
|
|