AI Medical Compendium Topic:
Brain Neoplasms

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Super-resolution of brain tumor MRI images based on deep learning.

Journal of applied clinical medical physics
INTRODUCTION: To explore and evaluate the performance of MRI-based brain tumor super-resolution generative adversarial network (MRBT-SR-GAN) for improving the MRI image resolution in brain tumors.

Application of Genetic Algorithm and U-Net in Brain Tumor Segmentation and Classification: A Deep Learning Approach.

Computational intelligence and neuroscience
The development of unusual cells in the cerebrum causes brain cancer. It is classified primarily into two classes: a noncarcinogenic (benign) type of growth and cancerous (malignant) growth. Early detection of this disease is a quintessential task fo...

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment.

Scientific reports
The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the acc...

Region Convolutional Neural Network for Brain Tumor Segmentation.

Computational intelligence and neuroscience
Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close s...

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...