Automatic detection of diabetic retinopathy retinal images using artificial neural network

The Diabetic Retinopathy (DR) is a critical vascular disorder that can cause a permanent blindness. Thus, the early recognition and the treatment are required to avoid major vision loss. Nowadays manual screening is done however, they are very incompetent to large image database of patients and most...

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Main Authors:	Syamimi Mardiah, Shaharum, Nurul Hajar, Hashim, Nurhafizah, Abu Talip, Mohamad Shaiful, Abdul Karim, Ahmad Afif, Mohd Faudzi
Format:	Conference or Workshop Item
Language:	English English
Published:	Springer Singapore 2019
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/25047/ http://umpir.ump.edu.my/id/eprint/25047/ http://umpir.ump.edu.my/id/eprint/25047/1/60.%20Automatic%20detection%20of%20diabetic%20retinopathy%20retinal%20images%20using%20artificial%20neural%20network.pdf http://umpir.ump.edu.my/id/eprint/25047/2/60.1%20Automatic%20detection%20of%20diabetic%20retinopathy%20retinal%20images%20using%20artificial%20neural%20network.pdf

id	ump-25047
recordtype	eprints
spelling	ump-250472019-12-09T03:44:32Z http://umpir.ump.edu.my/id/eprint/25047/ Automatic detection of diabetic retinopathy retinal images using artificial neural network Syamimi Mardiah, Shaharum Nurul Hajar, Hashim Nurhafizah, Abu Talip Mohamad Shaiful, Abdul Karim Ahmad Afif, Mohd Faudzi TK Electrical engineering. Electronics Nuclear engineering The Diabetic Retinopathy (DR) is a critical vascular disorder that can cause a permanent blindness. Thus, the early recognition and the treatment are required to avoid major vision loss. Nowadays manual screening is done however, they are very incompetent to large image database of patients and most importantly they are very time consuming. Besides, it required skilled professionals for the diagnosis. Automatic DR diagnosis systems can be as an optional method to the manual methods as they can significantly reduce the manual screening process labor. Screening conducted over a larger population can become effective if the system can distinguish between normal and abnormal cases, as a replacement for the manual examination of all images. Hence, the development of an Automated Diabetic Retinopathy detection systems has been recognized in the current times. This study has successfully developed an automated detection system for proliferative diabetic retinopathy symptoms using an artificial neural network with two types of feature used; mean of pixel and area of the pixel. The highest accuracy of this system is 90% with 30 hidden neurons in the neural network trained for all features. The results clearly show that the proposed method is effective for detection of Diabetic Retinopathy. Springer Singapore 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25047/1/60.%20Automatic%20detection%20of%20diabetic%20retinopathy%20retinal%20images%20using%20artificial%20neural%20network.pdf pdf en http://umpir.ump.edu.my/id/eprint/25047/2/60.1%20Automatic%20detection%20of%20diabetic%20retinopathy%20retinal%20images%20using%20artificial%20neural%20network.pdf Syamimi Mardiah, Shaharum and Nurul Hajar, Hashim and Nurhafizah, Abu Talip and Mohamad Shaiful, Abdul Karim and Ahmad Afif, Mohd Faudzi (2019) Automatic detection of diabetic retinopathy retinal images using artificial neural network. In: Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 27-28 September 2018 , Universiti Malaysia Pahang. pp. 495-503., 538. ISBN 978-981-13-3708-6 (Online) https://doi.org/10.1007/978-981-13-3708-6_43
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Syamimi Mardiah, Shaharum Nurul Hajar, Hashim Nurhafizah, Abu Talip Mohamad Shaiful, Abdul Karim Ahmad Afif, Mohd Faudzi Automatic detection of diabetic retinopathy retinal images using artificial neural network
description	The Diabetic Retinopathy (DR) is a critical vascular disorder that can cause a permanent blindness. Thus, the early recognition and the treatment are required to avoid major vision loss. Nowadays manual screening is done however, they are very incompetent to large image database of patients and most importantly they are very time consuming. Besides, it required skilled professionals for the diagnosis. Automatic DR diagnosis systems can be as an optional method to the manual methods as they can significantly reduce the manual screening process labor. Screening conducted over a larger population can become effective if the system can distinguish between normal and abnormal cases, as a replacement for the manual examination of all images. Hence, the development of an Automated Diabetic Retinopathy detection systems has been recognized in the current times. This study has successfully developed an automated detection system for proliferative diabetic retinopathy symptoms using an artificial neural network with two types of feature used; mean of pixel and area of the pixel. The highest accuracy of this system is 90% with 30 hidden neurons in the neural network trained for all features. The results clearly show that the proposed method is effective for detection of Diabetic Retinopathy.
format	Conference or Workshop Item
author	Syamimi Mardiah, Shaharum Nurul Hajar, Hashim Nurhafizah, Abu Talip Mohamad Shaiful, Abdul Karim Ahmad Afif, Mohd Faudzi
author_facet	Syamimi Mardiah, Shaharum Nurul Hajar, Hashim Nurhafizah, Abu Talip Mohamad Shaiful, Abdul Karim Ahmad Afif, Mohd Faudzi
author_sort	Syamimi Mardiah, Shaharum
title	Automatic detection of diabetic retinopathy retinal images using artificial neural network
title_short	Automatic detection of diabetic retinopathy retinal images using artificial neural network
title_full	Automatic detection of diabetic retinopathy retinal images using artificial neural network
title_fullStr	Automatic detection of diabetic retinopathy retinal images using artificial neural network
title_full_unstemmed	Automatic detection of diabetic retinopathy retinal images using artificial neural network
title_sort	automatic detection of diabetic retinopathy retinal images using artificial neural network
publisher	Springer Singapore
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25047/ http://umpir.ump.edu.my/id/eprint/25047/ http://umpir.ump.edu.my/id/eprint/25047/1/60.%20Automatic%20detection%20of%20diabetic%20retinopathy%20retinal%20images%20using%20artificial%20neural%20network.pdf http://umpir.ump.edu.my/id/eprint/25047/2/60.1%20Automatic%20detection%20of%20diabetic%20retinopathy%20retinal%20images%20using%20artificial%20neural%20network.pdf
first_indexed	2023-09-18T22:38:15Z
last_indexed	2023-09-18T22:38:15Z
_version_	1777416741369937920

Automatic detection of diabetic retinopathy retinal images using artificial neural network

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