An evolutionary approach for fMRI big data classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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