Machine learning and deep learning for brain tumor MRI image segmentation

MKH Khan, W Guo, J Liu, F Dong, Z Li… - Experimental …, 2023 - journals.sagepub.com
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …

A deep learning based four-fold approach to classify brain MRI: BTSCNet

J Chaki, M Woźniak - Biomedical Signal Processing and Control, 2023 - Elsevier
Incorrect diagnosis of brain tumor types prevent appropriate response to medical assistance
and reduces patients' chances of survival. Examining MRI images of the patient's brain …

MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images

Y Cao, W Zhou, M Zang, D An, Y Feng, B Yu - … Signal Processing and …, 2023 - Elsevier
More than half of brain tumors are malignant tumors, so there is a need for fast and accurate
segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) images …

A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images

N Cinar, A Ozcan, M Kaya - Biomedical Signal Processing and Control, 2022 - Elsevier
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 …

[HTML][HTML] Attention-based multimodal glioma segmentation with multi-attention layers for small-intensity dissimilarity

X Liu, S Hou, S Liu, W Ding, Y Zhang - Journal of King Saud University …, 2023 - Elsevier
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 …

A symmetrical approach to brain tumor segmentation in MRI using deep learning and threefold attention mechanism

Z Rahman, R Zhang, JA Bhutto - Symmetry, 2023 - mdpi.com
The symmetrical segmentation of brain tumor images is crucial for both clinical diagnosis
and computer-aided prognosis. Traditional manual methods are not only asymmetrical in …

Uncertainty quantification and attention-aware fusion guided multi-modal MR brain tumor segmentation

T Zhou, S Zhu - Computers in Biology and Medicine, 2023 - Elsevier
Brain tumor is one of the most aggressive cancers in the world, accurate brain tumor
segmentation plays a critical role in clinical diagnosis and treatment planning. Although …

MPEDA-Net: A lightweight brain tumor segmentation network using multi-perspective extraction and dense attention

H Luo, D Zhou, Y Cheng, S Wang - Biomedical Signal Processing and …, 2024 - Elsevier
Malignant brain tumors are highly deadly, necessitating the quickly precise segmentation of
tumor regions. Previously, clinicians manually classified brain tumor regions utilizing …

Multi-modal brain tumor segmentation via disentangled representation learning and region-aware contrastive learning

T Zhou - Pattern Recognition, 2024 - Elsevier
Brain tumors are threatening the life and health of people in the world. Automatic brain tumor
segmentation using multiple MR images is challenging in medical image analysis. It is …

[PDF][PDF] SDS-Net: A lightweight 3D convolutional neural network with multi-branch attention for multimodal brain tumor accurate segmentation

Q Wu, Y Pei, Z Cheng, X Hu, C Wang - Math. Biosci. Eng, 2023 - aimspress.com
The accurate and fast segmentation method of tumor regions in brain Magnetic Resonance
Imaging (MRI) is significant for clinical diagnosis, treatment and monitoring, given the …