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

• Published in: Multimedia tools and applications 2021, Vol.80 (1), p.899-919
• Main Authors: Mzoughi, Hiba, Njeh, Ines, Slima, Mohamed Ben, Ben Hamida, Ahmed, Mhiri, Chokri, Mahfoudh, Kheireddine Ben
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Brain; Brain cancer; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Diagnosis; Diagnostic software; Diagnostic systems; Exploration; Glioma; Ground truth; Machine learning; Magnetic resonance imaging; Medical research; Multimedia; Multimedia Information Systems; Neural networks; Noise; Noise reduction; Parameters; Performance evaluation; Segmentation; Special Purpose and Application-Based Systems; Support vector machines; Tumors
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09786-6

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.