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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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 |
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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 |