Needle Insertion Force Modeling using Genetic Programming Polynomial Higher Order Neural Network

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@InCollection{Fallahnezhad:2012:hoANNms,
  author =       "Mehdi Fallahnezhad and Hashem Yousefi",
  title =        "Needle Insertion Force Modeling using Genetic
                 Programming Polynomial Higher Order Neural Network",
  booktitle =    "Artificial Higher Order Neural Networks for Modeling
                 and Simulation",
  publisher =    "IGI Global",
  year =         "2012",
  editor =       "Ming Zhang",
  chapter =      "4",
  pages =        "58--76",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9781466621756",
  DOI =          "doi:10.4018/978-1-4666-2175-6.ch004",
  abstract =     "Precise insertion of a medical needle as an
                 end-effecter of a robotic or computer-aided system into
                 biological tissue is an important issue and should be
                 considered in different operations, such as brain
                 biopsy, prostate brachytherapy, and percutaneous
                 therapies. Proper understanding of the whole procedure
                 leads to a better performance by an operator or system.
                 In this chapter, the authors use a 0.98 mm diameter
                 needle with a real-time recording of force,
                 displacement, and velocity of needle through biological
                 tissue during in-vitro insertions. Using constant
                 velocity experiments from 5 mm/min up to 300 mm/min,
                 the data set for the force-displacement graph of
                 insertion was gathered. Tissue deformation with a small
                 puncture and a constant velocity penetration are the
                 two first phases in the needle insertion process.
                 Direct effects of different parameters and their
                 correlations during the process is being modelled using
                 a polynomial neural network. The authors develop
                 different networks in 2nd and 3rd order to model the
                 two first phases of insertion separately. Modelling
                 accuracies were 98percent and 86percent in phase 1 and
                 2, respectively.",
  notes =        "Norwegian University of Science and Technology (NTNU),
                 Norway, Amirkabir University of Technology (Tehran
                 Polytechnic), Iran",
}

Genetic Programming entries for Mehdi Fallahnezhad Hashem Yousefi

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