Background: Medulloblastoma is the most common malignant brain tumor in children. The important factors in predicting survival are the extent of disease, proliferation index of the tumor cells, histopathologic class and the age at diagnosis. The percentage of Ki-67 positive cells reflects the proliferation  of the cells and is one of the critical factors which determine the growth of the tumor....

Brain tumors show genetic heterogeneity and various immunohistochemical / biomarkers are now available for brain tumor diagnosis in addition to the advanced molecular techniques. However the basic routine histopathology remains the gold standard for diagnosis corroborating with the characteristic radiology and immunohistochemistry to substantiate or confirm diagnosis and MIB1/Ki67 for grading o...

Automatic brain tumor segmentation from multi-modality Magnetic Resonance Images (MRI) using deep learning methods plays an important role in assisting the diagnosis and treatment of tumor. However, previous mostly ignore latent relationship among different modalities. In this work, we propose a novel end-to-end Modality-Pairing method for segmentation. Paralleled branches are designed to explo...

Glioblastoma tumor recurrences often occur in brain tissue areas harboring infiltrating cells, resembling healthy imaging. Demarcating infiltrative regions for aggressive resections is critical improving prognostic outcomes but challenging neurosurgery. Herein, a multilayer sigmoid‐activated convolutional neural network (MLS‐CNN) developed rapidly distinguishing glioma infiltration histology. U...

Modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. For brain tumor detection, image segmentation is required, which is a challenging task faced by today’s medical neurologist. This is considered to be one of the most important step in detec...

Tumor classification and segmentation of brain magnetic resonance imaging (MRI) image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. Brain imaging segmentation is a complex and ...

An innovative imaging research is expected in the medical field due to challenges and inaccuracies diagnosing life-threatened harmful tumours. Brain tumor diagnosis one of most difficult areas study diagnostic imaging, with maximum fine for a small glitch given patients survival rate. Conventionally, biopsy method used identify tumour tissues from brain's soft by researchers (or) practitioners ...