Res-Net-VGG19: Improved tumor segmentation using MR images based on Res-Net architecture and efficient VGG gliomas grading

Abstract

Background: The determination of area tumor presents the chief challenge in brain tumor therapy and assessment. Without ionizing radiation, the medical Magnetic Resonance Imaging (MRI) tool has appeared as an essential diagnostic technique for brain cancers. Using 2D MRI images, manual segmentation of brain tumor size is a slow, error-prone task which the performance is extremely depends on operator's experience. In that respect, a consistent totally automated segmentation approach for the brain tumor detection is effectively needed to get a proficient dimension of the tumor size. Results: In this paper, an effusively computerized scheme for brain tumor detection is proposed by the use of deep convolutional networks. The proposed method was appraised on Brain Tumor Image Segmentation (BRATS 2020) datasets, including 1352 affected by brain tumor. Conclusion: Cross-validation technique has revealed that our process can attain talented segmentation competently reaching higher accuracy compared to other previous studies.

Keywords

• Brain tumor
• MRI images
• Res-net segmentation
• Low-grade gliomas
• High-grade gliomas
• Classification