Deep learning for brain tumor classification

Justin S. Paul; Andrew J. Plassard; Bennett A. Landman; Daniel Fabbri

doi:10.1117/12.2254195

13 March 2017 Deep learning for brain tumor classification

Justin S. Paul, Andrew J. Plassard, Bennett A. Landman, Daniel Fabbri

Proceedings Volume 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1013710 (2017) https://doi.org/10.1117/12.2254195
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

Conference Presentation

Citation Download Citation

Justin S. Paul, Andrew J. Plassard, Bennett A. Landman, and Daniel Fabbri "Deep learning for brain tumor classification", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1013710 (13 March 2017); https://doi.org/10.1117/12.2254195

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