Abstract #3256

Fully Connected Cascade Deep Architecture Neural Networks Outperform Support Vector Machines for Disease State Classification Using fMRI Data

Peng Wang¹, Bogdan Wilamowski, Gopikrishna Deshpande²

¹AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States; ²AU MRI Research Center, Department of Electrical and Computer Engineering; Department of Psychology, Auburn University, Auburn, AL, United States

Brain disorder classification is traditionally done by Support Vector Machines (SVMs) due to SVMs capability of handling data of high dimensionality and superior training speed. SVMs are effective in correctly identifying non-ADHD subjects. However SVMs are ineffective in correctly identifying ADHD subjects. Two-stage Fully Connected Cascade Deep neural network architecture has been designed and modified experimentally. This FCC Deep NN architecture significantly excels traditional NN architecture, overcomes data unbalance issue, is capable of handling data of high dimensionality and easy to train, generates better results, and therefore outperforms SVMs in total.