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Towards a computer aided diagnosis (CAD) for brain MRI glioblastomas tumor exploration based on a deep convolutional neuronal networks (D-CNN) architectures

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Abstract

Manuel brain glioblastomas tumor exploration through MRI modalities is time-consuming. It is considered as a harmful and critical task due to highly inhomogeneous tumor regions composition. For this reason, clinicians recommend the Computer-Aided Diagnosis (CAD) tools to ensure a more accurate diagnostic. Based on convolutional Deep-Learning algorithms, this paper investigates a fully automatic CAD for brain Glioblastomas tumors exploration including three steps: pre-processing, segmentation, and finally classification. A denoising and an automatic contrast enhancement method have been applied to preprocess the MRI scans. A Multi-Modal Cascaded U-net architecture, based on Fully Convolutional deep Network (FCN), has been adopted for the Region of Interest (ROI) extraction and finally, Deep Convolutional Neural Network (D-CNN) architecture has been used to classify brain glioblastomas tumor into High-Grade (HG) and Low-Grade (LG). Experiments were performed on the Multimodal Brain Tumor Segmentation Challenge BraTS-2018 datasets benchmark. Several validations metric have been adopted to assess the CAD’s performances. The Dice Metric (DM) parameter has been calculated between the obtained segmentation results and the available ground truth data. The accuracy parameter has been computed for classification performance evaluation. The higher DM and accuracy values could attest the performance and the efficiency of the proposed CAD tool.

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Data availability

The BraTS-2018 is available through the Image Processing Portal of the CBICA@UPenn (IPP - ipp.cbica.upenn.edu).

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Authors and Affiliations

  1. ATMS Advanced Technologies for Medecine and Signal, ENIS, Sfax University, Sfax, Tunisia

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

  2. National Engineering School of Gabes, Gabes University, Gabes, Tunisia

    Hiba Mzoughi

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

    Ines Njeh

  4. National School of Electronics and Telecommunications of Sfax, Sfax University, Sfax, Tunisia

    Mohamed Ben Slima

  5. National Engineering School of Sfax, Sfax University, Sfax, Tunisia

    Ahmed Ben Hamida

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

    Chokri Mhiri

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

    Kheireddine Ben Mahfoudh

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  1. Hiba Mzoughi
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Correspondence to Hiba Mzoughi.

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Mzoughi, H., Njeh, I., Slima, M.B. et al. Towards a computer aided diagnosis (CAD) for brain MRI glioblastomas tumor exploration based on a deep convolutional neuronal networks (D-CNN) architectures. Multimed Tools Appl 80, 899–919 (2021). https://doi.org/10.1007/s11042-020-09786-6

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  • DOI: https://doi.org/10.1007/s11042-020-09786-6

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