@inproceedings{scholars9090,
         journal = {Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           title = {Classification of fMRI data using support vector machine and convolutional neural network},
           pages = {324--329},
            note = {cited By 3; Conference of 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 ; Conference Date: 12 September 2017 Through 14 September 2017; Conference Code:132915},
            year = {2017},
             doi = {10.1109/ICSIPA.2017.8120630},
        abstract = {In recent years convolutional neural network have obtained more popularity because of its progressive performance for different applications especially for object recognition. In neuroimaging, data varies from person to person and condition to condition so it is always a challenging job to model the brain data. Any analysis in neuroimaging is also dependent on the quality of data and currently, functional magnetic resonance imaging is considered as the best among all techniques. It is most reliable and popular modality to measure the brain activity patterns. In fMRI, region of interest is a common method of analysis in which data is taken from a specific brain region based on the structural or functional information. In this study, convolutional neural network is applied to the significant voxels obtained through the t-contrast of the design matrix during the ROI analysis. Data is taken against two conditions and 1000 significant voxels with highest absolute values are taken for each condition for further analysis. During the proposed method, analysis is performed using convolutional neural network along with ROI analysis. Support vector machine is used in the classification of both methods; ROI and proposed methods. In conclusion, it is shown that the features extracted through convolutional neural network can provide better significant results compared to the other one. {\^A}{\copyright} 2017 IEEE.},
        keywords = {Brain; Convolution; Image processing; Image segmentation; Magnetic resonance imaging; Neural networks; Neuroimaging; Object recognition; Quality control; Support vector machines, Brain activity patterns; Convolutional neural network; fMRI; Functional information; Functional magnetic resonance imaging; Method of analysis; Quality of data; Region of interest, Functional neuroimaging},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041407169&doi=10.1109\%2fICSIPA.2017.8120630&partnerID=40&md5=84f4fd28fe08930bc63fe84d29b58bab},
            isbn = {9781509055593},
          author = {Zafar, R. and Malik, A. S. and Shuaibu, A. N. and Javvad Ur Rehman, M. and Dass, S. C.}
}