The Assessment of Time-Domain Features for Detecting Symptoms of Diabetic Retinopathy

Year 2016, Volume: 4 Issue: Special Issue-1, 136 - 140, 26.12.2016

Gülin Elibol , Semih Ergin

https://doi.org/10.18201/ijisae.270351

Cited By: 2

Abstract

 Diabetes affects the capillary vessels in retina
and causes vision loss. This disorder of retina due to diabetes is named as
Diabetic Retinopathy (DR). Diagnosing the stages of DR is performed on a
publicly available database (DiaraetDB1) via detecting the symptoms of this
disease. Time-domain features are extracted and selected to classify a fundus
image. Fisher’s Linear Discriminant Analysis (FLDA), Linear Bayes Normal
Classifier (LDC), Decision Tree (DT) and k-Nearest Neighbor (k-NN) are used as
the classification methods in the experimental benchmarking. The recognition
accuracies are obtained using all features (68 features) and selected features
separately. k-NN is observed as the best classification method for without
feature selection case and it gives averagely 92.22% accuracy. For feature
selection case, LDC gives the best average accuracy as 92.45% with maximum 7
carefully chosen features.

Keywords

Sequential feature selection, diabetic retinopathy, microaneurysms, hemorrhages, exudates

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