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Abstract #1325

Classification of brain tumors by 1H MRSI and MRI using convolutional neural networks

Jacopo Acquarelli1,2, Arend Heerschap3, Geert J. Postma2, Twan van Laarhoven1, Jeroen J. Jansen2, Elena Marchiori1, and Lutgarde M.C. Buydens2

1Data Science, Radboud University Nijmegen, Nijmegen, Netherlands, 2Analytical Chemistry, Radboud University Nijmegen, Nijmegen, Netherlands, 3Radiology and Nuclear Medicine, Radboud University Nijmegen, Nijmegen, Netherlands

Several machine learning approaches have been used to classify brain tumors using MR images and spectra. Here we explore the specific properties of convolutional neural networks (CNN) for this task. We designed a CNN that could be trained on combined MR image and spectroscopic image data by exploiting their specific properties (spatial and spectral locality). Using a ‘leave-one-out’ validation, we demonstrate that our method outperforms state-of-the-art classification methods to distinguish tumor grades. These results demonstrate that CNNs are a powerful approach for tumor classification using MRSI data.

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