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Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification

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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.

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Acknowledgments

The authors would to thank professors and doctors “Chokri Mhiri” and “Kheireddine Ben Mahfoudh” from the University Hospital Habib Bourguiba for their cooperation to achieve this work

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Authors and Affiliations

  1. Advanced Technologies for Medecine and Signal (ATMS), Sfax university, ENIS, Route de la Soukra km 4, 3038, Sfax, Tunisia

    Hiba Mzoughi, Ines Njeh, Mohamed Ben Slima & Ahmed BenHamida

  2. National Engineering School of Gabes, Gabes university, Avenue Omar Ibn El Khattab, Zrig Gabes, 6029, Gabes, Tunisia

    Hiba Mzoughi

  3. Higher Institute of Computer Science and Multimedia of Gabes, Gabes university, Gabes, Tunisia

    Ines Njeh

  4. National Engineering School of Sfax, Regim-Lab, Sfax university, Sfax, Tunisia

    Ali Wali & Ahmed BenHamida

  5. National School of Electronics and Telecommunications of Sfax, Sfax university, Sfax, Tunisia

    Mohamed Ben Slima

  6. Department of Neurology, Habib Bourguiba University Hospital, Sfax, Tunisia

    Chokri Mhiri

  7. Department of Radiology, Habib Bourguiba University Hospital, Sfax, Tunisia

    Kharedine Ben Mahfoudhe

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  1. Hiba Mzoughi
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Mzoughi, H., Njeh, I., Wali, A. et al. Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification. J Digit Imaging 33, 903–915 (2020). https://doi.org/10.1007/s10278-020-00347-9

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