AI Medical Compendium Topic:
Brain Neoplasms

Clear Filters Showing 931 to 940 of 1033 articles

CNN-based glioma detection in MRI: A deep learning approach.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: More than a million people are affected by brain tumors each year; high-grade gliomas (HGGs) and low-grade gliomas (LGGs) present serious diagnostic and treatment hurdles, resulting in shortened life expectancies. Glioma segmentation is s...

Improving Efficiency of Brain Tumor Classification Models Using Pruning Techniques.

Current medical imaging
BACKGROUND: This research investigates the impact of pruning on reducing the computational complexity of a five-layered Convolutional Neural Network (CNN) designed for classifying MRI brain tumors. The study focuses on enhancing the efficiency of the...

Brain tumor segmentation based on the U-NET+⁣+ network with efficientnet encoder.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Brain tumor is a highly destructive, aggressive, and fatal disease. The presence of brain tumors can disrupt the brain's ability to control body movements, consciousness, sensations, thoughts, speech, and memory. Brain tumors are often ac...

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review.

Critical reviews in oncogenesis
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extractio...

Design and Development of Hypertuned Deep learning Frameworks for Detection and Severity Grading of Brain Tumor using Medical Brain MR images.

Current medical imaging
BACKGROUND: Brain tumor is a grave illness causing worldwide fatalities. The current detection methods for brain tumors are manual, invasive, and rely on histopathological analysis. Determining the type of brain tumor after its detection relies on bi...

Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation.

Current medical imaging
BACKGROUND: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually.

Development and validation of a clinical prediction model for glioma grade using machine learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive.

Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.

Radiology. Artificial intelligence
Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting ...

Classification of Brain Tumours in MRI Images using a Convolutional Neural Network.

Current medical imaging
INTRODUCTION: Recent advances in deep learning have aided the well-being business in Medical Imaging of numerous disorders like brain tumours, a serious malignancy caused by unregulated and aberrant cell portioning. The most frequent and widely used ...

Whole-brain radiotherapy associated with structural changes resembling aging as determined by anatomic surface-based deep learning.

Neuro-oncology
BACKGROUND: Brain metastases are the most common intracranial tumors in adults and are associated with significant morbidity and mortality. Whole-brain radiotherapy (WBRT) is used frequently in patients for palliation, but can result in neurocognitiv...