Minimal training time in supervised retinal vessel segmentation

In this paper, we perform comparative analysis between different classifiers using the same experimental setup for supervised retinal vessel segmentation. The aim of this paper is to find supervised classifier that can obtain good segmentation accuracy with minimal training time. Minimizing the trai...

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Main Author: Che Azemin, Mohd Zulfaezal
Format: Proceeding Paper
Language:English
English
English
Published: IOS Press 2014
Subjects:
Online Access:http://irep.iium.edu.my/40793/
http://irep.iium.edu.my/40793/2/somet201446.pdf
http://irep.iium.edu.my/40793/5/front_page.pdf
http://irep.iium.edu.my/40793/8/40793_Minimal%20training%20time%20in%20supervised%20retinal%20vessel%20segmentation_SCOPUS.pdf
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author Che Azemin, Mohd Zulfaezal
author_facet Che Azemin, Mohd Zulfaezal
author_sort Che Azemin, Mohd Zulfaezal
building IIUM Repository
collection Online Access
description In this paper, we perform comparative analysis between different classifiers using the same experimental setup for supervised retinal vessel segmentation. The aim of this paper is to find supervised classifier that can obtain good segmentation accuracy with minimal training time. Minimizing the training time is essential when dealing with biomedical images. The more samples introduced to a learning model, the better it can adapt to the unseen data. The results indicate a trade-off between accuracy and training time can be obtained in a classifier trained by a Neural Network. When tested with a publicly available database, the learning model only requires less than 2 minutes in the learning phase and achieves overall accuracy of 94.54%.
first_indexed 2025-11-14T15:58:40Z
format Proceeding Paper
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institution International Islamic University Malaysia
institution_category Local University
language English
English
English
last_indexed 2025-11-14T15:58:40Z
publishDate 2014
publisher IOS Press
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spelling iium-407932017-09-21T12:34:32Z http://irep.iium.edu.my/40793/ Minimal training time in supervised retinal vessel segmentation Che Azemin, Mohd Zulfaezal RE Ophthalmology TA164 Bioengineering In this paper, we perform comparative analysis between different classifiers using the same experimental setup for supervised retinal vessel segmentation. The aim of this paper is to find supervised classifier that can obtain good segmentation accuracy with minimal training time. Minimizing the training time is essential when dealing with biomedical images. The more samples introduced to a learning model, the better it can adapt to the unseen data. The results indicate a trade-off between accuracy and training time can be obtained in a classifier trained by a Neural Network. When tested with a publicly available database, the learning model only requires less than 2 minutes in the learning phase and achieves overall accuracy of 94.54%. IOS Press 2014-09 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/40793/2/somet201446.pdf application/pdf en http://irep.iium.edu.my/40793/5/front_page.pdf application/pdf en http://irep.iium.edu.my/40793/8/40793_Minimal%20training%20time%20in%20supervised%20retinal%20vessel%20segmentation_SCOPUS.pdf Che Azemin, Mohd Zulfaezal (2014) Minimal training time in supervised retinal vessel segmentation. In: 13th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques (SoMeT_14), 22-24 Sept. 2014, Langkawi, Malaysia. http://ebooks.iospress.nl/volumearticle/37348 10.3233/978-1-61499-434-3-631
spellingShingle RE Ophthalmology
TA164 Bioengineering
Che Azemin, Mohd Zulfaezal
Minimal training time in supervised retinal vessel segmentation
title Minimal training time in supervised retinal vessel segmentation
title_full Minimal training time in supervised retinal vessel segmentation
title_fullStr Minimal training time in supervised retinal vessel segmentation
title_full_unstemmed Minimal training time in supervised retinal vessel segmentation
title_short Minimal training time in supervised retinal vessel segmentation
title_sort minimal training time in supervised retinal vessel segmentation
topic RE Ophthalmology
TA164 Bioengineering
url http://irep.iium.edu.my/40793/
http://irep.iium.edu.my/40793/
http://irep.iium.edu.my/40793/
http://irep.iium.edu.my/40793/2/somet201446.pdf
http://irep.iium.edu.my/40793/5/front_page.pdf
http://irep.iium.edu.my/40793/8/40793_Minimal%20training%20time%20in%20supervised%20retinal%20vessel%20segmentation_SCOPUS.pdf