Paper Info
Title
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-thebox 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: (x) over bar = 0.69, s = 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.
Year
DOI
Venue
2018
10.1007/978-3-030-01424-7_30
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III
Keywords
Field
DocType
Convolutional neural network, Class activation mapping, Migraine, Magnetic resonance imaging
Pattern recognition,Convolutional neural network,Computer science,Medical findings,Visualization,Anterior cingulate cortex,Artificial intelligence,Deep learning,Corpus callosum,Magnetic resonance imaging,Parietal lobe
Conference
Volume
ISSN
Citations 
11141
0302-9743
0
PageRank 
References 
Authors
0.34
4
5
Name
Order
Citations
PageRank
Hwei Geok Ng100.68
Matthias Kerzel2327.67
Jan Mehnert31268.28
Arne May4414.33
Stefan Wermter51100151.62