Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches

Abu, A. and Leow, L.K. and Ramli, R. and Omar, H. (2016) Classification of Suncus murinus species complex (Soricidae: Crocidurinae) in Peninsular Malaysia using image analysis and machine learning approaches. BMC Bioinformatics, 17 (S19). ISSN 1471-2105

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Official URL: http://dx.doi.org/10.1186/s12859-016-1362-5

Abstract

Background: Taxonomists frequently identify specimen from various populations based on the morphological characteristics and molecular data. This study looks into another invasive process in identification of house shrew (Suncus murinus) using image analysis and machine learning approaches. Thus, an automated identification system is developed to assist and simplify this task. In this study, seven descriptors namely area, convex area, major axis length, minor axis length, perimeter, equivalent diameter and extent which are based on the shape are used as features to represent digital image of skull that consists of dorsal, lateral and jaw views for each specimen. An Artificial Neural Network (ANN) is used as classifier to classify the skulls of S. murinus based on region (northern and southern populations of Peninsular Malaysia) and sex (adult male and female). Thus, specimen classification using Training data set and identification using Testing data set were performed through two stages of ANNs. Results: At present, the classifier used has achieved an accuracy of 100% based on skulls' views. Classification and identification to regions and sexes have also attained 72.5%, 87.5% and 80.0% of accuracy for dorsal, lateral, and jaw views, respectively. This results show that the shape characteristic features used are substantial because they can differentiate the specimens based on regions and sexes up to the accuracy of 80% and above. Finally, an application was developed and can be used for the scientific community. Conclusions: This automated system demonstrates the practicability of using computer-assisted systems in providing interesting alternative approach for quick and easy identification of unknown species.

Item Type: Article
Uncontrolled Keywords: Artificial intelligence; Automation; Classification (of information); Complex networks; Image classification; Learning systems; Neural networks; Population statistics; Statistical tests
Subjects: Q Science > Q Science (General)
Q Science > QH Natural history > QH301 Biology
Divisions: Faculty of Science > Institute of Biological Sciences
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 14 Aug 2017 03:47
Last Modified: 14 Aug 2017 03:48
URI: http://eprints.um.edu.my/id/eprint/17682

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