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GLDM and Tamura features based KNN and particle swarm optimization for automatic diabetic retinopathy recognition system

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Abstract

Diabetic retinopathy is one of the significant investigated topics in the last few years since blindness could be the outcome of unchecked and serious diabetic retinopathy cases. Detection and recognition of DR at the beginning can help in reducing the risk of vision loss and save health significantly. Therefore, this paper proposes three different approaches in terms of features extraction and classification. The proposed approaches introduced three classifiers which are SVM, KNN, and DA as well as three types of the statistical texture features techniques: GLDM, GLCM, and GLRLM. So that for every approach, each proposed classifier is tested on every single technique of adopted texture features independently, performing quite effective comparative study based on accuracy. The main objective of this study is to come up with an appropriate approach for DR recognition, in terms of accuracy enhancement. Therefore, this research proposes one more method based on particle swarm (PSO) for KNN classifier optimization and Tamura features. Tyler Coye algorithm is used for blood vessel segmentation in retinal images. The experiments are implemented based on retina images of the Drive dataset. As a result, the KNN based GLDM approach have gained an accuracy rate reached of (95%) while the announced optimization method of PSO-KNN on Tamura features achieved higher accuracy (100%) among other proposed approaches and state of art methods.

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  1. Department of Computer Sciences, College of Education for Pure Sciences, University of Basrah, Basrah, Iraq

    Entesar Barges & Eman Thabet

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Barges, E., Thabet, E. GLDM and Tamura features based KNN and particle swarm optimization for automatic diabetic retinopathy recognition system. Multimed Tools Appl 82, 271–295 (2023). https://doi.org/10.1007/s11042-022-13282-4

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