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Evolutionary Intelligence
Published in: Evolutionary Intelligence 1-2/2018

23-07-2018 | Special Issue

LVP extraction and triplet-based segmentation for diabetic retinopathy recognition

Authors: Santosh Nagnath Randive, Amol D. Rahulkar, Ranjan K. Senapati

Published in: Evolutionary Intelligence | Issue 1-2/2018

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Abstract

Till now, the detection of diabetic retinopathy seems to be one of the sensitive research topics since it is related to health care of any individual. A number of contributions in terms of detection already exists in the dice; still, there present some problems regarding the detection accuracy. This issue motivates to develop a new detection model of diabetic retinopathy, and moreover, this model tells the severity of retinopathy from the given fundus image. The proposed model includes preprocessing, segmentation, feature extraction and classification stages. Here, Triplet Half band Filterbank (THFB) Segmentation is performed, local vector pattern (LVP) is used for extracting the features, principle component analysis (PCA) procedure is used to reduce the dimensions of the feature vector, and neural network (NN) is used for classification purpose. The proposed model compares its performance over other conventional classifiers like support vector machine (SVM), k nearest neighbor (k-NN) and Navies Bayes (NB) in terms of positive and negative measures. The positive measures are accuracy, specificity, sensitivity, precision, negative predictive value (NPV), F1-Score and Matthews Correlation Coefficient (MCC). Similarly, the negative measures are the false positive rate (FPR), false negative rate (FNR) and false discovery rate (FDR), and the efficiency of the proposed model is proven.
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Metadata
Title
LVP extraction and triplet-based segmentation for diabetic retinopathy recognition
Authors
Santosh Nagnath Randive
Amol D. Rahulkar
Ranjan K. Senapati
Publication date
23-07-2018
Publisher
Springer Berlin Heidelberg
Published in
Evolutionary Intelligence / Issue 1-2/2018
Print ISSN: 1864-5909
Electronic ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-018-0158-0

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