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Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks
The use of various kinds of magnetic resonance imaging (MRI) techniques for examining
brain tissue has increased significantly in recent years, and manual investigation of each of …
brain tissue has increased significantly in recent years, and manual investigation of each of …
A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNET
Brain tumor, abnormal cell growth within the brain, require precise segmentation to facilitate
effective treatment planning. Accurately identifying tumor boundaries from complex Magnetic …
effective treatment planning. Accurately identifying tumor boundaries from complex Magnetic …
Deep Learning Based Segmentation Methods Applied to DDSM Images: A Review
Mammography is the first choice for screening of breast tissue for women aged 38 and
above. There are two types of mammographic images, ie digitized screen film mammograms …
above. There are two types of mammographic images, ie digitized screen film mammograms …
An intelligent LinkNet-34 model with EfficientNetB7 encoder for semantic segmentation of brain tumor
A brain tumor is an unnatural expansion of brain cells that can't be stopped, making it one of
the deadliest diseases of the nervous system. The brain tumor segmentation for its earlier …
the deadliest diseases of the nervous system. The brain tumor segmentation for its earlier …
HD-Former: A hierarchical dependency Transformer for medical image segmentation
H Wu, W Min, D Gai, Z Huang, Y Geng, Q Wang… - Computers in Biology …, 2024 - Elsevier
Medical image segmentation is a compelling fundamental problem and an important
auxiliary tool for clinical applications. Recently, the Transformer model has emerged as a …
auxiliary tool for clinical applications. Recently, the Transformer model has emerged as a …
Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network
M Ramkumar, RS Kumar… - … Journal of Imaging …, 2024 - Wiley Online Library
Brain tumor segmentation (BTS) from magnetic resonance imaging (MRI) scans is crucial for
the diagnosis, treatment planning, and monitoring of therapeutic results. Thus, this research …
the diagnosis, treatment planning, and monitoring of therapeutic results. Thus, this research …
[HTML][HTML] Improved Brain Tumor Segmentation in MR Images with a Modified U-Net
Detecting brain tumors is crucial in medical diagnostics due to the serious health risks these
abnormalities present to patients. Deep learning approaches can significantly improve …
abnormalities present to patients. Deep learning approaches can significantly improve …
MAEU‐NET: A novel supervised architecture for brain tumor segmentation
A brain tumor is an abnormal growth of cells that damages the neural system and may lead
to severe conditions. These cells are irregular in shape and size, and as a result, the manual …
to severe conditions. These cells are irregular in shape and size, and as a result, the manual …
Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space.
Early detection of brain tumors is critical for effective treatment planning. Identifying tumors in
their nascent stages can significantly enhance the chances of patient survival. While there …
their nascent stages can significantly enhance the chances of patient survival. While there …
Quantum‐inspired hybrid algorithm for image classification and segmentation: Q‐Means++ max‐cut method
SK Roy, B Rudra - International Journal of Imaging Systems …, 2024 - Wiley Online Library
Finding brain tumors is a crucial step in medical diagnosis that can have a big impact on
how patients turn out. Conventional detection techniques can be laborious and demand a lot …
how patients turn out. Conventional detection techniques can be laborious and demand a lot …