Deep learning has surged in popularity in recent years, notably in the domains of medical
image processing, medical image analysis, and bioinformatics. In this study, we offer a …

Thanks to the powerful representation learning ability, convolutional neural network has
been an effective tool for the brain tumor segmentation task. In this work, we design multiple …

The biopsy is a gold standard method for tumor grading. However, due to its invasive nature,
it has sometimes proved fatal for brain tumor patients. As a result, a non-invasive computer …

Gliomas are the most common and threatening brain tumors with little to no survival rate.
Accurate detection of such tumors is crucial for survival of the subject. Naturally, tumors have …

Composite restorations are widely used worldwide, but the polymerization shrinkage is their
main disadvantage that may lead to clinical failures and adverse consequences. This review …

The human brain is considered to be the anatomical seat of intelligence, comprehensively
supervising conscious and autonomous functions responsible for monitoring and control …

Brain tumor segmentation is a process of identifying the cancerous brain tissues and
labeling them automatically based on the tumor types. Manual segmentation of tumor from …

An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to
develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root …

Precise brain tumor segmentation can improve patient prognosis. However, due to the
complicated structure of the human brain, brain tumor segmentation is a challenging task. To …

We propose a framework for the robust and fully-automatic segmentation of magnetic
resonance (MR) brain images called “Multi-Atlas Label Propagation with Expectation …