Non-invasive technique of diabetes detection using iris images. (1st August 2019)

• Record Type: Journal Article
• Authors: Verma, Kesari; Singh, Bikesh Kumar; Agrawal, Neelam
• Is Part Of: International journal of computational vision and robotics. Volume 9:Number 4(2019)
• Page Start: 351
• Page End: 367
• Publication Date: 2019-08-01
• Subjects: diabetes detection -- image processing -- iris images -- support vector machine -- SVM -- artificial neural network -- ANN -- Gabor features -- grey level co-occurrence matrix -- GLCM -- non-invasive technique; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

Alternative medicine techniques are important in improving the quality of life, disease prevention and better to the conventional invasive method of diseases detection. This paper addresses a non-invasive approach of diabetic detection using iris images. The proposed techniques used to diagnose diabetes using modern digital image processing techniques that analyses structural properties of the iris and classifies the patterns according to iridology chart. The system analyses the broken tissues of the iris by extracting significant textural features using Gabor filter bank and grey level co-occurrence matrix (GLCM) from the specified subsection of the iris. The extracted textural features help to categorise the diabetic and non-diabetic irises using benchmarks artificial neural network (ANN) and support vector machine (SVM) classifiers. The promising results of extensive experiments demonstrate the effectiveness of the proposed method.