Magnetic Resonance Imaging Brain Segmentation Using Bi-Directional Convolutional Long Short-Term Memory U-Net With Densely Connected Convolutions

Magnetic Resonance Imaging Brain Segmentation Using Bi-Directional Convolutional Long Short-Term Memory U-Net With Densely Connected Convolutions

Despite advancements in imaging technologies, traditional diagnostic methods relying on manual interpretation by clinicians remain prone to errors. This underscores the need for robust automated segmentation techniques. Accurate three-dimensional (3D) Magnetic Resonance Imaging (MRI) brain tumor seg...

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Bibliographic Details
Main Authors: Meshari D. Alanazi, Amna Maraoui, Imen Werda, Ahmed Ben Atitallah, Turki M. Alanazi, Mohammed Albekairi, Anis Sahbani, Amr Yousef
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Bi-directional
brain segmentation
convolutional long short-term memory
densely connected convolutions
magnetic resonance imaging
U-Net
Online Access:https://ieeexplore.ieee.org/document/10967480/
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