KLASIFIKASI FASE RETINOPATI DIABETES MENGGUNAKAN BACKPROPAGATION NEURAL NETWORK

• Main Authors: , Rocky Yefrenes Dillak, , Drs. Agus Harjoko, M.Sc, Ph.D
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2012
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/99813/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=56213
• Institution: Universitas Gadjah Mada

) Diabetic retinopathy (DR is one of the complications on retina caused by diabetes. The study aims to develop a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). Ninety-seven retinal fundus images were used in this study. Six different texture features such as maximum probability, correlation, contrast, energy, homogeneity, and entropy were extracted from the digital fundus images using gray level cooccurence matrix (GLCM). The features were fed into a backpropagation neural network classifier for automatic classification. The proposed approach is able to classify with sensitivity 100%, specificity 100%, and accuracy 90.68%