Open AccessA Novel Approach for Diagnosis of Diabetes Using Iris Image Processing Technique and Evaluation Parameters
Author(s) -
Shilpa Jagtap,
J L Mudegaonkar,
Sanjay R. Patil,
Dinesh Bhoyar
Publication year - 2021
Publication title -
aijr proceedings
Language(s) - English
Resource type - Conference proceedings
ISSN - 2582-3922
DOI - 10.21467/proceedings.118.37
Subject(s) - artificial intelligence , pattern recognition (psychology) , normalization (sociology) , computer science , support vector machine , histogram , hamming distance , feature extraction , iris recognition , local binary patterns , hamming code , adaboost , gabor filter , convolutional neural network , wavelet transform , computer vision , biometrics , wavelet , decoding methods , algorithm , image (mathematics) , sociology , anthropology , block code
This paper presented here deals with study of identification and verification approach of Diabetes based on human iris pattern. In the pre-processing of this work, region of interest according to color (ROI) concept is used for iris localization, Dougman's rubber sheet model is used for normalization and Circular Hough Transform can be used for pupil and boundary detection. To extract features, Gabor Filter, Histogram of Oriented Gradients, five level decomposition of wavelet transforms likeHaar, db2, db4, bior 2.2, bior6.8 waveletscan be used. Binary coding scheme binaries’ the feature vector coefficients and classifier like hamming distance, Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), Neural Networks (NN), Random Forest (RF) and Linear Discriminative Analysis (LDA) with shrinkage parametercan be used for template matching. Performance parameters such as Computational time, Hamming distance variation, False Acceptance Rate (FAR), False Rejection Rate (FRR), Accuracy, and Match ratio can be calculated for the comparison purpose.