AIMC Topic: Brain Neoplasms

Clear Filters Showing 591 to 600 of 1218 articles

A data augmentation method for fully automatic brain tumor segmentation.

Computers in biology and medicine
Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimensional U-Net archit...

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

European radiology
OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.

MSFR-Net: Multi-modality and single-modality feature recalibration network for brain tumor segmentation.

Medical physics
BACKGROUND: Accurate and automated brain tumor segmentation from multi-modality MR images plays a significant role in tumor treatment. However, the existing approaches mainly focus on the fusion of multi-modality while ignoring the correlation betwee...

Metaheuristic Optimization-Driven Novel Deep Learning Approach for Brain Tumor Segmentation.

BioMed research international
Brain tumor has the foremost distinguished etiology of high morality. Neoplasm, a categorization of brain tumors, is very operative in distinguishing and determining the tumor's exact location in the brain. Magnetic resonance imaging (MRI) is an effi...

Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM.

Medicina (Kaunas, Lithuania)
Clinical diagnosis has become very significant in today's health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosi...

BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification.

Computational intelligence and neuroscience
Early detection of brain tumors can save precious human life. This work presents a fully automated design to classify brain tumors. The proposed scheme employs optimal deep learning features for the classification of FLAIR, T1, T2, and T1CE tumors. I...

Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging.

JAMA network open
IMPORTANCE: Deep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis.

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques.

Biomedical engineering online
BACKGROUND: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and te...

An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.

Tomography (Ann Arbor, Mich.)
A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis, as a minor human judgment can eventually cause severe consequences. Magnetic Resonance Image (MRI) ...

Deep learning for brain metastasis detection and segmentation in longitudinal MRI data.

Medical physics
PURPOSE: Brain metastases (BM) occur frequently in patients with metastatic cancer. Early and accurate detection of BM is essential for treatment planning and prognosis in radiation therapy. Due to their tiny sizes and relatively low contrast, small ...