An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences
| Autor: | Rémy Guillevin, Christine Fernandez-Maloigne, Matthieu Coupet, Mathieu Naudin, Thierry Urruty, Teerapong Leelanupab, Pascal Bourdon |
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| Přispěvatelé: | Synthèse et analyse d'images (XLIM-ASALI), XLIM (XLIM), Université de Limoges (UNILIM)-Centre National de la Recherche Scientifique (CNRS)-Université de Limoges (UNILIM)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Central nervous system 02 engineering and technology Brain tissue Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Empirical research [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Glioma 0202 electrical engineering electronic engineering information engineering medicine ComputingMilieux_MISCELLANEOUS medicine.diagnostic_test Artificial neural network business.industry Cancer Magnetic resonance imaging Pattern recognition medicine.disease Brain gliomas 3. Good health medicine.anatomical_structure Deep neural networks 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 27th International Conference on Neural Information Processing (ICONIP 2020) 27th International Conference on Neural Information Processing (ICONIP 2020), Nov 2020, Bangkok, Thailand. pp.113-125, ⟨10.1007/978-3-030-63830-6_10⟩ Neural Information Processing ISBN: 9783030638290 ICONIP (1) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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