An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Mansourvar:2015:plosone,

• author = "Marjan Mansourvar and Shahaboddin Shamshirband and Ram Gopal Raj and Roshan Gunalan and Iman Mazinani",
• title = "An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines",
• journal = "PLoS ONE",
• year = "2015",
• volume = "10",
• number = "9",
• month = sep # " 24",
• keywords = "genetic algorithms, genetic programming",
• publisher = "Public Library of Science",
• bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov",
• language = "en",
• oai = "oai:pubmedcentral.nih.gov:4581666",
• rights = "http://creativecommons.org/licenses/by/4.0/; This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited",
• URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4581666/",
• URL = "http://www.ncbi.nlm.nih.gov/pubmed/26402795",
• URL = "http://dx.doi.org/10.1371/journal.pone.0138493",
• DOI = "doi:10.1371/journal.pone.0138493",
• size = "14 pages",
• abstract = "Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalisation capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.",

}

Genetic Programming entries for Marjan Mansourvar Shahaboddin Shamshirband Ram Gopal Raj Roshan Gunalan Iman Mazinani

Citations