Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem

Diabetic Retinopathy (DR) is one of the most threatening disease which caused blindness for diabetic patient. With the increasing number of DR cases nowadays, diabetic eye screening has become a challenging task for ophthalmologist as they need to deal with a large number of retinal image to be d...

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Main Author:	Kader, Nur Izzati Ab
Format:	Thesis
Language:	English
Published:	2019
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/48838/1/Nur%20Izzati%20Binti%20Ab%20Kader_PCOM000516%28R%29%20cut.pdf
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id	my-usm-ep.48838
record_format	uketd_dc
spelling	my-usm-ep.488382021-04-12T04:50:51Z Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem 2019-03 Kader, Nur Izzati Ab QA75.5-76.95 Electronic computers. Computer science Diabetic Retinopathy (DR) is one of the most threatening disease which caused blindness for diabetic patient. With the increasing number of DR cases nowadays, diabetic eye screening has become a challenging task for ophthalmologist as they need to deal with a large number of retinal image to be diagnosed every day. Screening and early detection of DR play a vital role to help reducing the incidence of visual morbidity and vision loss. The screening task is done manually in most countries using qualitative scale to detect abnormalities on the retina. Although this approach is useful, the detection is not accurate. Previous researchers have tried a few attempts to propose an automatic DR classification, however it needs to be improvised especially in terms of accuracy. A group of literates showed that DR classification can be performed using the clinical features resulted from the blood test such as glycated haemoglobin, triglyceride, creatine and glucose value. Even this subject have been studied previously, but it remains the subject of on-going research. Hence, this research aims to obtain optimal or near-optimal performance value in the study of diabetic classification using supervised machine learning. There are many algorithms available for classification purpose such as k-Nearest Neighbour, k-Means, Support Vector Machine, Decision Tree, Artificial Neural Network and Linear Discriminant Analysis. Due to the success of many classification problems been proposed with good result, k-Nearest Neighbour, Artificial Neural Network, and Support Vector Machine algorithms are used in this research. 2019-03 Thesis http://eprints.usm.my/48838/ http://eprints.usm.my/48838/1/Nur%20Izzati%20Binti%20Ab%20Kader_PCOM000516%28R%29%20cut.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Kader, Nur Izzati Ab Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem
description	Diabetic Retinopathy (DR) is one of the most threatening disease which caused blindness for diabetic patient. With the increasing number of DR cases nowadays, diabetic eye screening has become a challenging task for ophthalmologist as they need to deal with a large number of retinal image to be diagnosed every day. Screening and early detection of DR play a vital role to help reducing the incidence of visual morbidity and vision loss. The screening task is done manually in most countries using qualitative scale to detect abnormalities on the retina. Although this approach is useful, the detection is not accurate. Previous researchers have tried a few attempts to propose an automatic DR classification, however it needs to be improvised especially in terms of accuracy. A group of literates showed that DR classification can be performed using the clinical features resulted from the blood test such as glycated haemoglobin, triglyceride, creatine and glucose value. Even this subject have been studied previously, but it remains the subject of on-going research. Hence, this research aims to obtain optimal or near-optimal performance value in the study of diabetic classification using supervised machine learning. There are many algorithms available for classification purpose such as k-Nearest Neighbour, k-Means, Support Vector Machine, Decision Tree, Artificial Neural Network and Linear Discriminant Analysis. Due to the success of many classification problems been proposed with good result, k-Nearest Neighbour, Artificial Neural Network, and Support Vector Machine algorithms are used in this research.
format	Thesis
qualification_level	Master's degree
author	Kader, Nur Izzati Ab
author_facet	Kader, Nur Izzati Ab
author_sort	Kader, Nur Izzati Ab
title	Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem
title_short	Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem
title_full	Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem
title_fullStr	Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem
title_full_unstemmed	Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem
title_sort	hybridization of optimized support vector machine and artificial neural network for the diabetic retinopathy classification problem
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2019
url	http://eprints.usm.my/48838/1/Nur%20Izzati%20Binti%20Ab%20Kader_PCOM000516%28R%29%20cut.pdf
_version_	1747821968993812480

Hybridization Of Optimized Support Vector Machine And Artificial Neural Network For The Diabetic Retinopathy Classification Problem

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