Classification of MRI Migraine Medical Data Using 3D Convolutional Neural Network

Abstract

While statistical approaches are being implemented in medical data analyses because of their high accuracy and efficiency, the use of deep learning computations can potentially provide out-of-the-box insights, especially when statistical approaches did not yield a good result. In this paper we classify migraine and non-migraine magnetic resonance imaging (MRI) data, using a deep learning method named convolutional neural network (CNN). 198 MRI scans, which were obtained equally from both data groups, resulted in the maximum classification test accuracy of 85% (validation accuracy: $\bar{x}$ = 0.69, $\sigma$ = 0.06), compared to the baseline statistical accuracy of 50%. We then used class activation mapping (CAM) method to visualize brain regions that the CNN model took to distinguish one data group from the other and the visualization pointed at the parietal lobe, corpus callosum, brain stem and anterior cingulate cortex, of which the brain stem was mentioned in the medical findings for white matter abnormalities. Our findings suggest that CNN and CAM combined can be a useful image-based data analysis tool to add inspiration or discussion in the medical problem-solving process.

The authors from Universität Hamburg gratefully acknowledge partial support from the German Research Foundation DFG under project CML (TRR 169).