An evolutionary approach for fMRI big data classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{tahmassebi:2017:CEC,
  author =       "Amirhessam Tahmassebi and Amir H. Gandomi and 
                 Ian McCann and Mieke H. J. Schulte and Lianne Schmaal and 
                 Anna E. Goudriaan and Anke Meyer-Baese",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "An evolutionary approach for {fMRI} big data
                 classification",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "1029--1036",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Resting-state function magnetic resonance imaging
                 (fMRI) images allow us to see the level of activity in
                 a patient's brain. We consider fMRI of patients before
                 and after they underwent a smoking cessation treatment.
                 Two classes of patients have been studied here, that
                 one took the drug N-acetylcysteine and the ones took a
                 placebo. Our goal was to classify the relapse in
                 nicotine-dependent patients as treatment or
                 non-treatment based on their fMRI scans. The image
                 slices of brain are used as the variable and as results
                 here we deal with a big data problem with about 240,000
                 inputs. To handle this problem, the data had to be
                 reduced and the first process in doing that was to
                 create a mask to apply to all images. The mask was
                 created by averaging the before images for all patients
                 and selecting the top 40percent of voxels from that
                 average. This mask was then applied to all fMRI images
                 for all patients. The average of the difference in the
                 before treatment and after fMRI images for each patient
                 were found and these were flattened to one dimension.
                 Then a matrix was made by stacking these 1D arrays on
                 top of each other and a data reduction algorithm was
                 applied on it. Lastly, this matrix was fed into some
                 machine learning and Genetic Programming algorithms and
                 leave-one-out cross-validation was used to test the
                 accuracy. Out of all the data reduction machine
                 learning algorithms used, the best accuracy was
                 obtained using Principal Component Analysis along with
                 Genetic Programming classifier. This gave an accuracy
                 of 74percent, which we consider significant enough to
                 suggest that there is a difference in the resting-state
                 fMRI images of a smoker that undergoes this smoking
                 cessation treatment compared to a smoker that receives
                 a placebo.",
  keywords =     "genetic algorithms, genetic programming, Big Data,
                 biomedical MRI, brain, data reduction, drugs, image
                 classification, learning (artificial intelligence),
                 medical image processing, patient treatment, principal
                 component analysis, N-acetylcysteine drug, brain image
                 slices, data reduction algorithm, evolutionary
                 approach, fMRI big data classification, fMRI images,
                 function magnetic resonance imaging, genetic
                 programming classifier, image masking, machine
                 learning, nicotine-dependent patients, placebo drug,
                 relapse classification, smoking cessation treatment,
                 Blood, Correlation, Feature extraction, Machine
                 learning algorithms, Magnetic resonance imaging",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969421",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969421}",
}

Genetic Programming entries for Amirhessam Tahmassebi A H Gandomi Ian McCann Mieke H J Schulte Lianne Schmaal Anna E Goudriaan Anke Meyer-Baese

Citations