DETECTION AND CLASSIFICATION OF NON-PROLIFERATIVE DIABETIC RETINOPATHY STAGES USING SUPPORT VECTOR MACHINE (SVM) AND ARTIFICIAL NEURAL NETWORK(ANN)

Khairunnisah, Khairunnisah (2016) DETECTION AND CLASSIFICATION OF NON-PROLIFERATIVE DIABETIC RETINOPATHY STAGES USING SUPPORT VECTOR MACHINE (SVM) AND ARTIFICIAL NEURAL NETWORK(ANN). Tugas Akhir (S1) - thesis, UNIVERSITAS BAKRIE.

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Abstract

Diabetic Retinopathy is the damage of blood vessels which can cause blindness. The detection of diabetic retinopathy is usually carried out by ophthalmologist by analysing fundus photography. In order to get accurate result, the fundus photography can be graded by up to four different ophthalmologists. This standard is highly impossible to achieve in rural areas with the shortage of qualified medical ophthalmologists. Therefore, an automated detection of diabetic retinopathy will be needed in order to ease the burden of ophthalmologist. Furthermore, it can also be used as training module for retinal screeners and graders. This research provides an automated detection and classification of non-proliferative diabetic retinopathy(NPDR). More specifically, this research uses Support Vector Machine(SVM) and Artificial Neural Network(ANN). The classification of diabetic retinopathy is grouped into the respective stage of non-proliferative diabetic retinopathy: normal (No DR detected), mild, and severe.

Item Type:	Thesis (Tugas Akhir (S1) - )
Uncontrolled Keywords:	Non-proliferative Diabetic Retinopathy(NPDR), Support Vector Machine(SVM), Artificial Neural Network(ANN), ophthalmologist
Subjects:	Computer Science > Computer - Software Thesis > Thesis (S1)
Divisions:	Fakultas Teknik dan Ilmu Komputer > Program Studi Informatika
Depositing User:	Khairunnisah
Date Deposited:	16 Sep 2016 08:35
Last Modified:	16 Sep 2016 08:35
URI:	http://repository.bakrie.ac.id/id/eprint/401

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