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An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences
Author(s)
Coupet, Matthieu
Urruty, Thierry
Leelanupab, Teerapong
Naudin, Mathieu
Bourdon, Pascal
Fernandez-Maloigne, Christine
Guillevin, Rémy
Date Issued
January 1, 2020
Type
Conference Paper
Abstract
Gliomas are the most common central nervous system tumors. They represent 1.3% of cancers and are the 15th most common cancer for men and women. For the diagnosis of such pathology, doctors commonly use Magnetic Resonance Imaging (MRI) with different sequences. In this work, we propose a global framework using convolutional neural networks to create an intelligent assistant system for neurologists to diagnose the brain gliomas. Within this framework, we study the performance of different neural networks on four MRI modalities. This work allows us to highlight the most specific MRI sequences so that the presence of gliomas in brain tissue can be classified. We also visually analyze extracted features from the different modalities and networks with an aim to improve the interpretability and analysis of the performance obtained. We apply our study on the MRI sequences that are obtained from BraTS datasets.
Citation
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12532 LNCS, 113-125, 2020