Search Results for author: Mohamed Mabrok

Found 3 papers, 0 papers with code

Neuro-TransUNet: Segmentation of stroke lesion in MRI using transformers

no code implementations10 Jun 2024 Muhammad Nouman, Mohamed Mabrok, Essam A. Rashed

Accurate segmentation of the stroke lesions using magnetic resonance imaging (MRI) is associated with difficulties due to the complicated anatomy of the brain and the different properties of the lesions.

Anatomy Lesion Segmentation +1

Transformers-based architectures for stroke segmentation: A review

no code implementations27 Mar 2024 Yalda Zafari-Ghadim, Essam A. Rashed, Mohamed Mabrok

Stroke remains a significant global health concern, necessitating precise and efficient diagnostic tools for timely intervention and improved patient outcomes.

Computational Efficiency Segmentation

Brain Stroke Segmentation Using Deep Learning Models: A Comparative Study

no code implementations25 Mar 2024 Ahmed Soliman, Yousif Yousif, Ahmed Ibrahim, Yalda Zafari-Ghadim, Essam A. Rashed, Mohamed Mabrok

In this study, we selected four types of deep models that were recently proposed and evaluated their performance for stroke segmentation: a pure Transformer-based architecture (DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention mechanisms in their design, an advanced hybrid model that incorporates CNNs with Transformers (FCT), and the well- known self-adaptive nnUNet framework with its configuration based on given data.

Image Segmentation Medical Image Segmentation +2

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