Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers, applied to MRI: a survey
Management of brain tumors is based on clinical and radiological information with
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
Brain tumor segmentation from multi-modal MR images via ensembling UNets
Glioma is a type of severe brain tumor, and its accurate segmentation is useful in surgery
planning and progression evaluation. Based on different biological properties, the glioma …
planning and progression evaluation. Based on different biological properties, the glioma …
A Systematic Review on Medical Image Segmentation using Deep Learning
Medical image segmentation is an essential step in various diagnostic and treatment
procedures. This study aimed to conduct a systematic review of state-of-the-art segmentation …
procedures. This study aimed to conduct a systematic review of state-of-the-art segmentation …
[HTML][HTML] Unfolding explainable AI for brain tumor segmentation
Brain tumor segmentation (BTS) has been studied from handcrafted engineered features to
conventional machine learning (ML) methods, followed by the cutting-edge deep learning …
conventional machine learning (ML) methods, followed by the cutting-edge deep learning …
An improved capsule network for glioma segmentation on MRI images: A curriculum learning approach
Glioma segmentation is an essential step in tumor identification and treatment planning.
Glioma segmentation is a challenging task because it appears with blurred and irregular …
Glioma segmentation is a challenging task because it appears with blurred and irregular …
Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique
Brain tumor classification is a critical task in medical imaging, as accurate diagnosis directly
influences treatment planning and patient outcomes. Traditional methods often fall short in …
influences treatment planning and patient outcomes. Traditional methods often fall short in …
Spatiotemporal analysis of speckle dynamics to track invisible needle in ultrasound sequences using convolutional neural networks: a phantom study
Purpose Accurate needle placement into the target point is critical for ultrasound
interventions like biopsies and epidural injections. However, aligning the needle to the thin …
interventions like biopsies and epidural injections. However, aligning the needle to the thin …
Brain Tumor Segmentation From Multi-Modal MR Images via Ensembling UNets: Yue Zhang, Pinyuan Zhong, Dabin Jie, Jiewei Wu, Shanmei Zeng, Jianping Chu …
Y Zhang, P Zhong, D Jie, J Wu, S Zeng, J Chu… - Frontiers in …, 2021 - patrinum.ch
Résumé Glioma is a type of severe brain tumor, and its accurate segmentation is useful in
surgery planning and progression evaluation. Based on different biological properties, the …
surgery planning and progression evaluation. Based on different biological properties, the …
DiffSwinTr: A diffusion model using 3D Swin Transformer for brain tumor segmentation
J Zhu, H Zhu, Z Jia, P Ma - International Journal of Imaging …, 2024 - Wiley Online Library
Automatic medical image segmentation has shown great potential in recent years.
Howerver, magnetic resonance images (MRI) usually have the characteristics of noise and …
Howerver, magnetic resonance images (MRI) usually have the characteristics of noise and …
Automated evaluation and parameter estimation of brain tumor using deep learning techniques
B Vijayakumari, N Kiruthiga, CP Bushkala - Neural Computing and …, 2024 - Springer
The identification and region extraction of brain tumors is an essential aspect of clinical
image analysis and the diagnosis of brain-related illnesses. The precise and accurate …
image analysis and the diagnosis of brain-related illnesses. The precise and accurate …