Abstract
Benign epilepsy with centrotemporal spikes (BECT) is the most common epilepsy in the children. The research of BECT mainly focuses on the comparative analysis of the BECT patients and the healthy controls. Different from the existing methods, we proposed a 3D convolution neural network (3DCNN) that directly predicts the disease of BECT from raw magnetic resonance imaging (MRI). The experiment shows our 3DCNN model get an \(89.80\%\) accuracy in the five-fold cross-validation evaluation which is over a large margin than the benchmark method.
Supported by the science and technology department of Sichuan province (No. 18MZGC0127).
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Yan, M., Liu, L., Chen, S., Pan, Y. (2018). A Deep Learning Method for Prediction of Benign Epilepsy with Centrotemporal Spikes. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_24
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