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 …
Image segmentation for MR brain tumor detection using machine learning: A Review
Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain
disease and monitor treatment as non-invasive imaging technology. MRI produces three …
disease and monitor treatment as non-invasive imaging technology. MRI produces three …
SwinBTS: A method for 3D multimodal brain tumor segmentation using swin transformer
Y Jiang, Y Zhang, X Lin, J Dong, T Cheng, J Liang - Brain sciences, 2022 - mdpi.com
Brain tumor semantic segmentation is a critical medical image processing work, which aids
clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural …
clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural …
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 …
Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors
The automatic segmentation of brain tumors in Magnetic Resonance Imaging (MRI) plays a
major role in accurate diagnosis and treatment planning. The present study proposes a new …
major role in accurate diagnosis and treatment planning. The present study proposes a new …
Hybrid dilation and attention residual U-Net for medical image segmentation
Z Wang, Y Zou, PX Liu - Computers in biology and medicine, 2021 - Elsevier
Medical image segmentation is a typical task in medical image processing and critical
foundation in medical image analysis. U-Net is well-liked in medical image segmentation …
foundation in medical image analysis. U-Net is well-liked in medical image segmentation …
Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information
Blood clots in the brain are frequently caused by brain tumors. Early detection of these clots
has the potential to significantly lower morbidity and mortality in cases of brain cancer. It is …
has the potential to significantly lower morbidity and mortality in cases of brain cancer. It is …
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 …
Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images
Among researchers using traditional and new machine learning and deep learning
techniques, 2D medical image segmentation models are popular. Additionally, 3D …
techniques, 2D medical image segmentation models are popular. Additionally, 3D …
Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data
Many machine learning-based studies have been carried out in the literature for the
detection of brain tumors using MRI data and most of what has been done in the last 6 years …
detection of brain tumors using MRI data and most of what has been done in the last 6 years …