BRAIN TUMOR DETECTION AND MULTI GRADE SEGMENTATION THROUGH HYBRID CAPS-VGGNET MODEL
Keywords:
Brain tumor detection, multi-grade segmentation, Capsule Networks, VGGNet, MRI, deep learning, medical imaging.Abstract
Brain tumor detection and multi-grade segmentation are critical tasks in medical imaging, particularly in Magnetic Resonance Imaging (MRI). Accurate identification and classification of tumor regions are essential for effective diagnosis and treatment planning. This study proposes a hybrid model combining Capsule Networks (CapsNet) and VGGNet for enhanced brain tumor detection and multi-grade segmentation. The model leverages the strengths of CapsNet in capturing spatial hierarchies and VGGNet's deep feature extraction capabilities. Experimental results demonstrate the efficacy of the proposed model in accurately detecting and segmenting brain tumors across different grades, offering a promising approach for automated medical image analysis.
Downloads
References
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for segmentation. biomedical image International Conference
on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234-241.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 770-778.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
NonCommercial — You may not use the material for commercial purposes.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
