Abstract
Few studies have focused on the potential of applying deep learning algorithms into magnetic resonance imaging (MRI) for automatic recognition of subjects with mild cognitive impairment (MCI). In this work, we propose the expedited convolutional neural networks involving Tucker decomposition to recognize MCI using MRI images. We employ transfer learning and data augmentation to deal with limited training data. The effect of Tucker decomposition on saving computational time is discussed. The experimental results show that the proposed model outperforms the previous methods. The expedited convolutional neural networks can provide a good guidance for the applications of deep learning in real-world classification with large training dataset.
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Acknowledgment
This work was supported by Shenzhen Basic Research Projects (Grant No. JCYJ 20160531184426303 and JCYJ 20150401150223648), National Natural Science Foundations of China (Grants No. 61502473 and No. 61503368), and Natural Science Foundation of Guangdong Province (Grant No. 2014A030310154 and No. 2016A030313176).
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Wang, S., Shen, Y., Chen, W., Xiao, T., Hu, J. (2017). Automatic Recognition of Mild Cognitive Impairment from MRI Images Using Expedited Convolutional Neural Networks. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10613. Springer, Cham. https://doi.org/10.1007/978-3-319-68600-4_43
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DOI: https://doi.org/10.1007/978-3-319-68600-4_43
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