[HTML][HTML] Efficient U-Net architecture with multiple encoders and attention mechanism decoders for brain tumor segmentation

I Aboussaleh, J Riffi, KE Fazazy, MA Mahraz, H Tairi - Diagnostics, 2023 - mdpi.com
The brain is the center of human control and communication. Hence, it is very important to
protect it and provide ideal conditions for it to function. Brain cancer remains one of the …

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 …

Automatic and accurate abnormality detection from brain MR images using a novel hybrid UnetResNext-50 deep CNN model

HM Rai, K Chatterjee, S Dashkevich - Biomedical Signal Processing and …, 2021 - Elsevier
The automatic and accurate detection and segmentation of brain tumors is a very tedious
and challenging task for medical experts and radiologists. This paper proposes a hybrid …

Artificial intelligence in tumor subregion analysis based on medical imaging: A review

M Lin, JF Wynne, B Zhou, T Wang, Y Lei… - Journal of Applied …, 2021 - Wiley Online Library
Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial
intelligence (AI) has achieved tremendous success in medical image analysis. This paper …

[PDF][PDF] SGEResU-Net for brain tumor segmentation

D Liu, N Sheng, T He, W Wang, J Zhang… - Math. Biosci. Eng, 2022 - aimspress.com
The precise segmentation of tumor regions plays a pivotal role in the diagnosis and
treatment of brain tumors. However, due to the variable location, size, and shape of brain …

An MRI-based deep learning approach for efficient classification of brain tumors

EU Haq, H Jianjun, K Li, HU Haq, T Zhang - Journal of Ambient …, 2023 - Springer
Efficient and reliable identification and classification of brain tumors from imaging data is
essential in the diagnosis and treatment of brain cancer cells. Magnetic resonance imaging …

Modality-level cross-connection and attentional feature fusion based deep neural network for multi-modal brain tumor segmentation

T Zhou - Biomedical Signal Processing and Control, 2023 - Elsevier
Brain tumor segmentation from Magnetic Resonance Imaging is essential for early diagnosis
and treatment planning for brain cancers in clinical practice. However, existing brain tumor …

[HTML][HTML] Transformers and their application to medical image processing: A review

D Zhu, D Wang - Journal of Radiation Research and Applied Sciences, 2023 - Elsevier
Transformers perform well in natural language processing tasks and have made many
breakthroughs in computer vision. In medical image processing, transformers are …

[PDF][PDF] Second-order ResU-Net for automatic MRI brain tumor segmentation

N Sheng, D Liu, J Zhang, C Che, J Zhang - Math. Biosci. Eng, 2021 - aimspress.com
Tumor segmentation using magnetic resonance imaging (MRI) plays a significant role in
assisting brain tumor diagnosis and treatment. Recently, U-Net architecture with its variants …

IDRM: Brain tumor image segmentation with boosted RIME optimization

W Zhu, L Fang, X Ye, M Medani… - Computers in Biology …, 2023 - Elsevier
Timely diagnosis of medical conditions can significantly mitigate the risks they pose to
human life. Consequently, there is an urgent demand for an effective auxiliary model that …