Classification Between Norm and Pathology Based on Brain MRI Scans Using Deep Learning

Student: Safiullin Amir

Educational Programme: Data Science (Master)

Year of Graduation: 2017

This study aims at tackling the problem of structural brain MRI classification using the classification of Alzheimer’s disease as an example. In this work I proposed deep 3D convolutional neural networks architectures and tested them on one of the largest open dataset for Alzheimer’s Disease National Initiative. The obtained results show that the convolutional neural networks can be successfully used for the classification of three-dimensional medical images, in spite of the low sample size and high dimensionality of the data. Main advance of the proposed approach is the lack of feature extraction stage, which allows the use of this approach for fast prediction in case of automatisation of skull stripping and intensity normalization steps. Key words: Machine Learning, Neuroimaging, Classification, Neural Networks.

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