Brain image segmentation in recent years: A narrative review
Brain image segmentation is one of the most time-consuming and challenging procedures in
a clinical environment. Recently, a drastic increase in the number of brain disorders has …
a clinical environment. Recently, a drastic increase in the number of brain disorders has …
Overview of multi-modal brain tumor mr image segmentation
The precise segmentation of brain tumor images is a vital step towards accurate diagnosis
and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate …
and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate …
A novel approach for brain tumour detection using deep learning based technique
Identifying the tumour's extent is a major challenge in planning treatment for brain tumours
and correctly measuring their size. Magnetic resonance imaging (MRI) has emerged as a …
and correctly measuring their size. Magnetic resonance imaging (MRI) has emerged as a …
dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI
Glioma is the most prevalent and dangerous type of brain tumor which can be life-
threatening when its grade is high. The early detection of these tumors can improve and …
threatening when its grade is high. The early detection of these tumors can improve and …
U-Net-based models towards optimal MR brain image segmentation
Brain tumor segmentation from MRIs has always been a challenging task for radiologists,
therefore, an automatic and generalized system to address this task is needed. Among all …
therefore, an automatic and generalized system to address this task is needed. Among all …
A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images
Several techniques are used to detect brain tumors in the medical research field; however,
Magnetic Resonance Imaging (MRI) is still the most effective technique used by experts …
Magnetic Resonance Imaging (MRI) is still the most effective technique used by experts …
Tmd-unet: Triple-unet with multi-scale input features and dense skip connection for medical image segmentation
Deep learning is one of the most effective approaches to medical image processing
applications. Network models are being studied more and more for medical image …
applications. Network models are being studied more and more for medical image …
A Multiple Layer U-Net, Un-Net, for Liver and Liver Tumor Segmentation in CT
ST Tran, CH Cheng, DG Liu - IEEE Access, 2020 - ieeexplore.ieee.org
Medical image segmentation is one of the crucial tasks in diagnosis as well as pre-surgery.
Recently, deep learning has significantly contributed to improving the efficiency of medical …
Recently, deep learning has significantly contributed to improving the efficiency of medical …
[HTML][HTML] Attention-based multimodal glioma segmentation with multi-attention layers for small-intensity dissimilarity
The segmentation of glioma by computer vision is one of the hot topics in medical image
analysis, which further helps doctors to make a better treatment plan for glioma. At present …
analysis, which further helps doctors to make a better treatment plan for glioma. At present …
[HTML][HTML] An optimized XGBoost technique for accurate brain tumor detection using feature selection and image segmentation
CJ Tseng, C Tang - Healthcare Analytics, 2023 - Elsevier
An abnormal multiplication of cells in the brain forms malignant and benign brain tumors.
Malignant brain tumors are more prevalent than benign ones. Detecting a tumor's physical …
Malignant brain tumors are more prevalent than benign ones. Detecting a tumor's physical …