LVP extraction and triplet-based segmentation for diabetic retinopathy recognition
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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.
KeywordsDiabetic retinopathy THFB segmentation Local vector pattern Feature extraction Neural network classification
We acknowledged our sincere thanks to Dr. Amol D Rahulkar, National Institute of Technology, Goa and Pimpri Chinchwad Education Trust’s Pimpri Chichwad College of Engineering & Research, Ravet, Pune for their encouragement and valuable support during this research work.
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