A Critical Review on Segmentation of Glioma Brain Tumor and Prediction of Overall Survival
N Rasool, JI Bhat - Archives of Computational Methods in Engineering, 2024 - Springer
In recent years, the surge in glioma brain tumor cases has positioned it as the 10th most
prevalent tumor affecting individuals across diverse age groups. Gliomas, characterized by …
prevalent tumor affecting individuals across diverse age groups. Gliomas, characterized by …
Dynamic weighted knowledge distillation for brain tumor segmentation
D An, P Liu, Y Feng, P Ding, W Zhou, B Yu - Pattern Recognition, 2024 - Elsevier
Automatic 3D MRI brain tumor segmentation holds a crucial position in the field of medical
image analysis, contributing significantly to the clinical diagnosis and treatment of brain …
image analysis, contributing significantly to the clinical diagnosis and treatment of brain …
Brain tumor segmentation using partial depthwise separable convolutions
Gliomas are the most common and aggressive form of all brain tumors, with medial survival
rates of less than two years for the highest grade. While accurate and reproducible …
rates of less than two years for the highest grade. While accurate and reproducible …
Yaru3DFPN: a lightweight modified 3D UNet with feature pyramid network and combine thresholding for brain tumor segmentation
Gliomas are the most common and aggressive form of all brain tumors, with a median
survival rate of fewer than two years, especially for the highest-grade glioma patient …
survival rate of fewer than two years, especially for the highest-grade glioma patient …
BrainSegNeT: a lightweight brain tumor segmentation model based on U-net and progressive neuron expansion
Brain tumor segmentation is a critical task in medical image analysis. In recent years,
several deep learning-based models have been developed for brain tumor segmentation …
several deep learning-based models have been developed for brain tumor segmentation …
Enhancing Brain Tumor Detection and Classification with Reduced Complexity Spatial Fusion Convolutional Neural Networks.
OH Kesav, R GK - International Journal of Intelligent …, 2024 - search.ebscohost.com
In this study, we propose a novel enhanced deep learning method for the detection and
classification of brain tumours known as the reduced complexity spatial fusion CNN (RCSF …
classification of brain tumours known as the reduced complexity spatial fusion CNN (RCSF …
[PDF][PDF] Brain Cancer Object Segmentation Using LPSIT Method and Back Propagation Network.
B Nisha, MV Jose - International Journal of Intelligent Engineering & …, 2023 - inass.org
Brain cancer is the 10th leading cause of death for men and women. Magnetic resonance
imaging (MRI) based Brain cancer segmentation is challenged by less accuracy and high …
imaging (MRI) based Brain cancer segmentation is challenged by less accuracy and high …