Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification
- PDF / 1,601,307 Bytes
- 13 Pages / 595.276 x 790.866 pts Page_size
- 51 Downloads / 363 Views
Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification Hiba Mzoughi 1,2 & Ines Njeh 1,3 & Ali Wali 4 & Mohamed Ben Slima 1,5 & Ahmed BenHamida 1,4 & Chokri Mhiri 6 & Kharedine Ben Mahfoudhe 7
# Society for Imaging Informatics in Medicine 2020
Abstract Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture has the potential to merge both the local and global contextual information with reduced weights. To overcome the data heterogeneity, we proposed a preprocessing technique based on intensity normalization and adaptive contrast enhancement of MRI data. Furthermore, for an effective training of such a deep 3D network, we used a data augmentation technique. The paper studied the impact of the proposed preprocessing and data augmentation on classification accuracy. Quantitative evaluations, over the well-known benchmark (Brats-2018), attest that the proposed architecture generates the most discriminative feature map to distinguish between LG and HG gliomas compared with 2D CNN variant. The proposed approach offers promising results outperforming the recently supervised and unsupervised state-of-the-art approaches by achieving an overall accuracy of 96.49% using the validation dataset. The obtained experimental results confirm that adequate MRI’s preprocessing and data augmentation could lead to an accurate classification when exploiting CNN-based approaches. Keywords Classification . 3D convolutional neural network (CNN) . Magnetic resonance imaging (MRI) . Gliomas . Classification . Deep learning
Introduction * Hiba Mzoughi [email protected] 1
Advanced Technologies for Medecine and Signal (ATMS), Sfax university, ENIS, Route de la Soukra km 4, 3038 Sfax, Tunisia
2
National Engineering School of Gabes, Gabes university, Avenue Omar Ibn El Khattab, Zrig Gabes 6029, Gabes, Tunisia
3
Higher Institute of Computer Science and Multimedia of Gabes, Gabes university, Gabes, Tunisia
4
National Engineering School of Sfax, Regim-Lab, Sfax university, Sfax, Tunisia
5
National School of Electronics and Telecommunications of Sfax, Sfax university, Sfax, Tunisia
6
Department of Neurology, Habib Bourguiba University Hospital, Sfax, Tunisia
7
Department of Radiology, Habib Bourguiba University Hospital, Sfax, Tunisia
Early and accurate detection of brain tumor grade has a direct impact not only on the patient’s estimated survival but also on treatment planning and tumor growth evaluation. Among the central nervous
Data Loading...
