AIMC Topic: Brain Neoplasms

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A deep ensemble learning framework for brain tumor classification using data balancing and fine-tuning.

Scientific reports
Brain tumors are a critical medical challenge, requiring accurate and timely diagnosis to improve patient outcomes. Misclassification can significantly reduce life expectancy, emphasizing the need for precise diagnostic methods. Manual analysis of ex...

Detection and classification of brain tumor using a hybrid learning model in CT scan images.

Scientific reports
Accurate diagnosis of brain tumors is critical in understanding the prognosis in terms of the type, growth rate, location, removal strategy, and overall well-being of the patients. Among different modalities used for the detection and classification ...

Deep intelligence: a four-stage deep network for accurate brain tumor segmentation.

Scientific reports
Image segmentation is an essential research field in image processing that has developed from traditional processing techniques to modern deep learning methods. In medical image processing, the primary goal of the segmentation process is to segment o...

Improved pharmacokinetic parameter estimation from DCE-MRI via spatial-temporal information-driven unsupervised learning.

Physics in medicine and biology
Pharmacokinetic (PK) parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide quantitative characterization of tissue perfusion and permeability. However, existing deep learning methods for PK parameter estimatio...

Support vector machine-based preoperative identification of IDH-Mutant low-grade gliomas in adult gliomas using clinical features.

BMC neurology
BACKGROUND: The preoperative identification of (isocitrate dehydrogenase) IDH-mutant low-grade gliomas (LGGs) is critical for personalized treatment planning. We aimed to develop a streamlined machine-learning model using key clinical features for ra...

Comprehensive brain tumour concealment utilizing peak valley filtering and deeplab segmentation.

Scientific reports
Brain tumour identification, segmentation cataloguing from MRI images is most thought-provoking and is a very much essential for many medical image analysis applications. Every brain imaging modality provides information about various parts of the tu...

Survival risk stratification of 2021 WHO glioblastoma by MRI radiomics and biological exploration.

BMC cancer
BACKGROUND: There is variability in overall survival among 2021 World Health Organization isocitrate dehydrogenase wild type glioblastoma (IDH-wt GBM) patients. The aim of the study was to develop a combined model for stratifying survival risk in IDH...

Enhanced image registration based brain tumour segmentation using optical particle swarm intelligence technique with Resnet Inceptionv2 HCNN.

Scientific reports
A brain tumor is the deadliest disease to cause sudden death, affecting billions of people worldwide. Artificial Intelligence (AI) powered technologies play a vital role in screening medical images to identify brain-suspecting tissue regions of attai...

Deep transfer learning based feature fusion model with Bonobo optimization algorithm for enhanced brain tumor segmentation and classification through biomedical imaging.

Scientific reports
The brain tumour (BT) is an aggressive disease among others, which leads to a very short life expectancy. Therefore, early and prompt treatment is the main stage in enhancing patients' quality of life. Biomedical imaging permits the non-invasive eval...

BLVRA promotes glioblastoma progression by regulating fatty acid metabolism.

Scientific reports
Fatty acid metabolism is critically involved in glioblastoma (GBM) pathogenesis; however, its regulatory mechanisms remain incompletely understood. In this study, we identified biliverdin reductase A (BLVRA) as a novel metabolic driver and prognostic...