Brain tumor detection and classification using machine learning: a comprehensive survey
J Amin, M Sharif, A Haldorai, M Yasmin… - Complex & intelligent …, 2022 - Springer
Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …
phase, it may lead to death. Despite many significant efforts and promising outcomes in this …
An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review
Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
Deep learning for brain tumor segmentation: a survey of state-of-the-art
Quantitative analysis of the brain tumors provides valuable information for understanding the
tumor characteristics and treatment planning better. The accurate segmentation of lesions …
tumor characteristics and treatment planning better. The accurate segmentation of lesions …
Weighted average ensemble deep learning model for stratification of brain tumor in MRI images
Brain tumor diagnosis at an early stage can improve the chances of successful treatment
and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures …
and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures …
A comprehensive survey on brain tumor diagnosis using deep learning and emerging hybrid techniques with multi-modal MR image
The brain tumor is considered the deadly disease of the century. At present, neuroscience
and artificial intelligence conspire in the timely delineation, detection, and classification of …
and artificial intelligence conspire in the timely delineation, detection, and classification of …
[HTML][HTML] A review on brain tumor segmentation based on deep learning methods with federated learning techniques
Brain tumors have become a severe medical complication in recent years due to their high
fatality rate. Radiologists segment the tumor manually, which is time-consuming, error …
fatality rate. Radiologists segment the tumor manually, which is time-consuming, error …
HOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detection
Brain tumor happens due to the instant and uncontrolled cell growth. It may lead to death if
not cured at an early stage. In spite of several promising results and substantial efforts in this …
not cured at an early stage. In spite of several promising results and substantial efforts in this …
DPAFNet: A residual dual-path attention-fusion convolutional neural network for multimodal brain tumor segmentation
Y Chang, Z Zheng, Y Sun, M Zhao, Y Lu… - … Signal Processing and …, 2023 - Elsevier
Brain tumors are highly hazardous, and precise automated segmentation of brain tumor
subregions has great importance and research significance on the diagnosis and treatment …
subregions has great importance and research significance on the diagnosis and treatment …
Generalizability of machine learning models: quantitative evaluation of three methodological pitfalls
Purpose To investigate the impact of the following three methodological pitfalls on model
generalizability:(a) violation of the independence assumption,(b) model evaluation with an …
generalizability:(a) violation of the independence assumption,(b) model evaluation with an …
Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis
EJ van Kempen, M Post, M Mannil, RL Witkam… - European …, 2021 - Springer
Objectives Different machine learning algorithms (MLAs) for automated segmentation of
gliomas have been reported in the literature. Automated segmentation of different tumor …
gliomas have been reported in the literature. Automated segmentation of different tumor …