Exudates Detection from Digital Fundus Images Using GLCM Features with Decision Tree Classifier
作者： Parashuram Bannigidad , Asmita Deshpande
DOI: 10.1007/978-981-13-9184-2_22
关键词:
摘要: Diabetes affects a number of human organs, the most common organ being eye. Diabetic Retinopathy, Glaucoma, Macular Edema are some ophthalmic disorders found in diabetic patients. Ophthalmologists diagnose Retinopathy digital fundus image with presence exudates. The proposed algorithm consolidates morphological operations for blood vessel removal, segmentation and optic disc removal followed by exudates detection. In this experiment GLCM features extracted. These enhance detection affected regions retinal as it depicts how frequently various combinations gray levels co-exist an section. This also explores use SVM, k-NN Decision tree classifiers to distinguish between diseased healthy images. It is observed from experimentation that classifier yields best results classifying PPV decision 100% DIARETDB0, 97.6% DIARETDB1, 97% e-Ophtha EX Messidor databases. sensitivity 91.6% 94.5% exhibits 100%, 97.6%, accuracy values databases, respectively using classifier. Thus, robust means detecting examining
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