Latest AI and machine learning research in brain cancer for healthcare professionals.
In the present paper, a hybrid multilevel thresholding technique that combines intuitionistic fuzzy ...
Gliomas are the most common type of tumor in the brain. Although the definite diagnosis is routinely...
Isocitrate dehydrogenase () mutations in glioma patients confer longer survival and may guide treat...
PURPOSE: To investigate deep reinforcement learning (DRL) based on historical treatment plans for de...
Limited therapeutic options exist for inoperable bilateral kidney tumors. We report the first ever u...
BACKGROUND: One approach to improving the personalized treatment of cancer is to understand the cell...
Cancer is caused by germline and somatic mutations, which can share biological features such as amin...
The aim of this study was to investigate the impact of pixel-based machine learning (ML) techniques,...
Mummy berry, caused by Monilinia vaccinii-corymbosi, causes economic losses of highbush blueberry in...
The fundamental technique of performing percutaneous cardiovascular (CV) interventions has remained ...
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical im...
MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two im...
INTRODUCTION: American Indian/Alaska Native (AI/AN) patients with cancer disproportionally present w...
PURPOSE: Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Ma...
Hereditary haemorrhagic telangiectasia (HHT), also known as Osler-Weber-Rendu disease, is a rare, va...
Solar energy is considered as one of the main sources for renewable energy in the near future. Howev...
Robotic technology has been utilized in cardiovascular medicine for over a decade, and over that per...
 Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pi...
Shortwave solar radiation is an important component of the surface energy balance and provides the p...
Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modaliti...
This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segment...