AI Medical Compendium Topic:
Brain Neoplasms

Clear Filters Showing 971 to 980 of 1033 articles

Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.

Neuro-oncology
BACKGROUND: Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and ra...

Evolution of Deep Learning Algorithms for MRI-Based Brain Tumor Image Segmentation.

Critical reviews in biomedical engineering
Brain tumor textures are among the most challenging features for neuroradiologists to extract from magnetic resonance images (MRIs). Exceptionally high-grade tumors such as gliomas require quick and precise diagnosis and medical intervention due to t...

Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach.

Technology in cancer research & treatment
Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. We propose the use of a deep learning and transfer learning appr...

A Review on Deep Learning Architecture and Methods for MRI Brain Tumour Segmentation.

Current medical imaging
BACKGROUND: The automatic segmentation of brain tumour from MRI medical images is mainly covered in this review. Recently, state-of-the-art performance is provided by deep learning- based approaches in the field of image classification, segmentation,...

Intelligent medical image feature extraction method based on improved deep learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Medical patients can be diagnosed early, however it is difficult to extract effective features in medical image segmentation based on semantic information.

Differentiation of rare brain tumors through unsupervised machine learning: Clinical significance of in-depth methylation and copy number profiling illustrated through an unusual case of IDH wildtype glioblastoma.

Clinical neuropathology
Methylation profiling has become a mainstay in brain tumor diagnostics since the introduction of the first publicly available classification tool by the German Cancer Research Center in 2017. We demonstrate the capability of this system through an ex...

Brain Tumor Segmentation of T1w MRI Images Based on Clustering Using Dimensionality Reduction Random Projection Technique.

Current medical imaging
BACKGROUND: Early diagnosis of a brain tumor may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. The availability of high-dimensional medical ...

DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data.

Neuroinformatics
With the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation task is the structured organization of a dataset for preserving integrity and ensuring reusability. Th...

An efficient approach to diagnose brain tumors through deep CNN.

Mathematical biosciences and engineering : MBE
BACKGROUND AND OBJECTIVE: Brain tumors are among the most common complications with debilitating or even death potential. Timely detection of brain tumors particularly at an early stage can lead to successful treatment of the patients. In this regard...

Brain tumor classification in MRI image using convolutional neural network.

Mathematical biosciences and engineering : MBE
Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual le...