An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  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",
  language =     "en",
  oai =          "",
  rights =       "; This is
                 an open access article distributed under the terms of
                 the Creative Commons Attribution License
                 ( , which
                 permits unrestricted use, distribution, and
                 reproduction in any medium, provided the original
                 author and source are credited",
  URL =          "",
  URL =          "",
  URL =          "",
  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