Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks

MK Abd-Ellah, AI Awad, AAM Khalaf, AM Ibraheem - Scientific reports, 2024 - nature.com
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 …

A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNET

K Pani, I Chawla - Computers and Electrical Engineering, 2024 - Elsevier
Brain tumor, abnormal cell growth within the brain, require precise segmentation to facilitate
effective treatment planning. Accurately identifying tumor boundaries from complex Magnetic …

Deep Learning Based Segmentation Methods Applied to DDSM Images: A Review

J Rani, J Singh, J Virmani - Archives of Computational Methods in …, 2025 - Springer
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 …

An intelligent LinkNet-34 model with EfficientNetB7 encoder for semantic segmentation of brain tumor

A Sulaiman, V Anand, S Gupta, MS Al Reshan… - Scientific Reports, 2024 - nature.com
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 …

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 …

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 …

[HTML][HTML] Improved Brain Tumor Segmentation in MR Images with a Modified U-Net

H Alquran, M Alslatie, A Rababah, WA Mustafa - Applied Sciences, 2024 - mdpi.com
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 …

MAEU‐NET: A novel supervised architecture for brain tumor segmentation

S Kumar, B Biswal - International Journal of Imaging Systems …, 2024 - Wiley Online Library
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 …

Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space.

M Khalil, MI Sharif, A Naeem… - Computers …, 2023 - search.ebscohost.com
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 …

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 …