Identifying central and peripheral nerve fibres with an artificial intelligence approach
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http://hdl.handle.net/10045/74415
Título: | Identifying central and peripheral nerve fibres with an artificial intelligence approach |
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Autor/es: | Gil, David | Girela, Jose L. | Azorin-Lopez, Jorge | Juan, Alba de | Juan Herrero, Joaquín de |
Grupo/s de investigación o GITE: | Lucentia | Biotecnología | Informática Industrial y Redes de Computadores |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Biotecnología |
Palabras clave: | Artificial neural network | K-means clustering | Decision trees | Decision support system | Nerve fibres | Multi-level classifier |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores | Biología Celular |
Fecha de publicación: | jun-2018 |
Editor: | Elsevier |
Cita bibliográfica: | Applied Soft Computing. 2018, 67: 276-285. doi:10.1016/j.asoc.2018.03.010 |
Resumen: | Distinguishing axons from central or peripheral nervous systems (CNS or PNS, respectively) is often a complicated task. The main objective of this work was to facilitate and support the process of automatically distinguishing the different types of nerve fibres by analysing their morphological characteristics. Our approach was based on a multi-level hierarchical classifier architecture that can handle the complexity of directly identifying nerve-fibre groups belonging to either the CNS or the PNS. The approach adopted comprises supervised methods (multilayer perceptron and decision trees), which are responsible for distinguishing the origin of the axons (CNS or PNS), whereas the unsupervised method (K-means clustering) performs nerve fibre clustering based on similar characteristics for both the CNS and PNS. Our experiments produced results with an accuracy higher than 88%. Our findings suggest that the development and implementation of a multi-level system improves automation capabilities and increases accuracy in the classification of nerves. Furthermore, our architecture allows for generalisation and flexibility, which can subsequently be extended to other biological control systems. |
Patrocinador/es: | This work was partially supported by the following grants: The Office of the Vice Chancellor for Research, Development, and Innovation, University of Alicante, Spain, (Grant Vigrob-137 to JDJ); the Chair of Reproductive Medicine, University of Alicante-Bernabeu Institute of Reproductive medicine (Grant 4-12I to JDJ). The Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) TIN2015-63502-C3-3-R, by the University of Alicante, within the program of support for research, under project GRE14-10, and by the Conselleria de Educación, Investigación, Cultura y Deporte, Comunidad Valenciana, Spain, within the program of support for research, under project GV/2016/087. |
URI: | http://hdl.handle.net/10045/74415 |
ISSN: | 1568-4946 (Print) | 1872-9681 (Online) |
DOI: | 10.1016/j.asoc.2018.03.010 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2018 Elsevier B.V. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.asoc.2018.03.010 |
Aparece en las colecciones: | INV - I2RC - Artículos de Revistas INV - LUCENTIA - Artículos de Revistas INV - GIDBT - Artículos de Revistas INV - AIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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2018_Gil_etal_ApplSoftComp_final.pdf | Versión final (acceso restringido) | 1,54 MB | Adobe PDF | Abrir Solicitar una copia |
2018_Gil_etal_ApplSoftComp_accepted.pdf | Accepted Manuscript (acceso abierto) | 1,96 MB | Adobe PDF | Abrir Vista previa |
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