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Deep learning generative adversarial network model for automated detection of diabetic retinopathy

Diabetic retinopathy (DR) is a leading disease that cause impaired vision with a consequence of permanent blindness if it is undiagnosed and untreated at the early stages. Alas, DR often has no early warning sign and may cause no symptoms. Particularly, recent statistics recorded that about 382 mill...

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Main Authors: Zainal Abidin, Nadzurah, Ismail, Amelia Ritahani, Amir Hussin, Amir 'Aatieff, Shafie, M L, Ridzuan, A N M
Format: Conference or Workshop Item
Language:English
English
English
Published: 2021
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/94161/1/EURECA%20Deep%20Learning%20Generative%20Adversarial%20Network%20Model%20for%20Automated%20Detection%20of%20Diabetic%20Retinopathy.pdf
http://irep.iium.edu.my/94161/2/3.%2016th%20Eureca%20Parallel%20Sessions%20v1.4.pdf
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spelling my.iium.irep.941612022-01-07T07:00:32Z http://irep.iium.edu.my/94161/ Deep learning generative adversarial network model for automated detection of diabetic retinopathy Zainal Abidin, Nadzurah Ismail, Amelia Ritahani Amir Hussin, Amir 'Aatieff Shafie, M L Ridzuan, A N M QA75 Electronic computers. Computer science Diabetic retinopathy (DR) is a leading disease that cause impaired vision with a consequence of permanent blindness if it is undiagnosed and untreated at the early stages. Alas, DR often has no early warning sign and may cause no symptoms. Particularly, recent statistics recorded that about 382 million individuals globally, with the number predicted to rise to 592 million by 2030 are suffers from DR. Due to the obvious large number of DR patients and limited medical resources in particular areas, patients with DR may not be treated in time, therefore missing out the best treatment options and eventually leading to irreversible vision loss. Unfortunately, a manual diagnosis to examine DR is tedious, time consuming, and error-prone, besides the consequences of manual interpretation which is highly dependent on the medical expert experiences to identify the presence of small features and significance of DR. This manual method opens to the inconsistency of the diagnosis. Thus, Automated Diabetic Retinopathy Detection aims to reduce the burden on ophthalmologists and mitigate diagnostic inconsistencies between manual readers by classifying DR stages using previous DR images with stages labels using Deep Learning. Generative Adversarial Network (GAN) is one of the major improvement of deep learning with potential to enhance the performance of automated detection significance of DR. Two different experiments were conducted and compared resulting in the best result with GAN evaluated by Frechet Inception Distance (FID), precision and recall. 2021-11-24 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/94161/1/EURECA%20Deep%20Learning%20Generative%20Adversarial%20Network%20Model%20for%20Automated%20Detection%20of%20Diabetic%20Retinopathy.pdf application/pdf en http://irep.iium.edu.my/94161/2/3.%2016th%20Eureca%20Parallel%20Sessions%20v1.4.pdf application/pdf en http://irep.iium.edu.my/94161/3/Eureca.png Zainal Abidin, Nadzurah and Ismail, Amelia Ritahani and Amir Hussin, Amir 'Aatieff and Shafie, M L and Ridzuan, A N M (2021) Deep learning generative adversarial network model for automated detection of diabetic retinopathy. In: 6th International Engineering and Computing Research Conference ( Eureca 2021 ), 24th November 2021, Virtual. (Unpublished)
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Zainal Abidin, Nadzurah
Ismail, Amelia Ritahani
Amir Hussin, Amir 'Aatieff
Shafie, M L
Ridzuan, A N M
Deep learning generative adversarial network model for automated detection of diabetic retinopathy
description Diabetic retinopathy (DR) is a leading disease that cause impaired vision with a consequence of permanent blindness if it is undiagnosed and untreated at the early stages. Alas, DR often has no early warning sign and may cause no symptoms. Particularly, recent statistics recorded that about 382 million individuals globally, with the number predicted to rise to 592 million by 2030 are suffers from DR. Due to the obvious large number of DR patients and limited medical resources in particular areas, patients with DR may not be treated in time, therefore missing out the best treatment options and eventually leading to irreversible vision loss. Unfortunately, a manual diagnosis to examine DR is tedious, time consuming, and error-prone, besides the consequences of manual interpretation which is highly dependent on the medical expert experiences to identify the presence of small features and significance of DR. This manual method opens to the inconsistency of the diagnosis. Thus, Automated Diabetic Retinopathy Detection aims to reduce the burden on ophthalmologists and mitigate diagnostic inconsistencies between manual readers by classifying DR stages using previous DR images with stages labels using Deep Learning. Generative Adversarial Network (GAN) is one of the major improvement of deep learning with potential to enhance the performance of automated detection significance of DR. Two different experiments were conducted and compared resulting in the best result with GAN evaluated by Frechet Inception Distance (FID), precision and recall.
format Conference or Workshop Item
author Zainal Abidin, Nadzurah
Ismail, Amelia Ritahani
Amir Hussin, Amir 'Aatieff
Shafie, M L
Ridzuan, A N M
author_facet Zainal Abidin, Nadzurah
Ismail, Amelia Ritahani
Amir Hussin, Amir 'Aatieff
Shafie, M L
Ridzuan, A N M
author_sort Zainal Abidin, Nadzurah
title Deep learning generative adversarial network model for automated detection of diabetic retinopathy
title_short Deep learning generative adversarial network model for automated detection of diabetic retinopathy
title_full Deep learning generative adversarial network model for automated detection of diabetic retinopathy
title_fullStr Deep learning generative adversarial network model for automated detection of diabetic retinopathy
title_full_unstemmed Deep learning generative adversarial network model for automated detection of diabetic retinopathy
title_sort deep learning generative adversarial network model for automated detection of diabetic retinopathy
publishDate 2021
url http://irep.iium.edu.my/94161/1/EURECA%20Deep%20Learning%20Generative%20Adversarial%20Network%20Model%20for%20Automated%20Detection%20of%20Diabetic%20Retinopathy.pdf
http://irep.iium.edu.my/94161/2/3.%2016th%20Eureca%20Parallel%20Sessions%20v1.4.pdf
http://irep.iium.edu.my/94161/3/Eureca.png
http://irep.iium.edu.my/94161/
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score 13.222552

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