Minimal training time in supervised retinal vessel segmentation

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.

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Abstract

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%.

Item Type:	Conference or Workshop Item (Plenary Papers)
Additional Information:	6768/40793 (New Trends in Software Methodologies, Tools and Techniques, H. Fujita et al. (Eds.) IOS Press, 2014, doi:10.3233/978-1-61499-434-3-631) (ISBN: 978-1-61499-433-6, e-ISBN: 978-1-61499-434-3)
Uncontrolled Keywords:	vessel segmentation, supervised learning, retinal blood vessel.
Subjects:	R Medicine > RE Ophthalmology T Technology > TA Engineering (General). Civil engineering (General) > TA164 Bioengineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Allied Health Sciences > Department of Optometry and Visual Science
Depositing User:	Dr. Mohd Zulfaezal Che Azemin
Date Deposited:	26 Jan 2015 12:03
Last Modified:	21 Sep 2017 20:34
URI:	http://irep.iium.edu.my/id/eprint/40793

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