Multi-Channel Deep Neural Network For Temporal Lobe Epilepsy Classification Using Multimodal Mri Data

Abstract

Multiple magnetic resonance imaging (MRI) modalities are currently used for the diagnosis and characterization of temporal lobe epilepsy (TLE). The objective of this study is to assess the performance of individual and combination of multimodal MRI datasets to provide an accurate classification of TLE by employing a multi-channel deep neural network. Several multi-channel deep neural network models were trained, validated, and tested using brain structure metrics from structural MRI, MRI-based region of interest correlation features, and personal demographic and cognitive data (PDC). The results show that PDC individually offered the most accurate classification of TLE followed by the combination of PDC with MRI-based brain structure metrics. These findings demonstrate the potential of deep learning approaches such as mDNN models to combine multiple datasets for TLE classification.