Image Enhancement and U-Net Based Brain Tumor Segmentation Using MRI

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This study demonstrates enhanced image segmentation using U-Net for brain tumors in MRI, suggesting improved detection methods.

The proposed model achieved a dice coefficient of 0.89, indicating high segmentation accuracy for brain tumors.
Using classical U-Net in this approach supports effective image segmentation processes in medical imaging.
Image processing techniques in pre- and post-processing significantly improved the model's overall performance.
These findings highlight the importance of early detection in treating brain tumors, utilizing advanced AI technologies.

Abdalgadir et al. (2025) studied this question.

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View Full Paper

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Authors

This study demonstrates enhanced image segmentation using U-Net for brain tumors in MRI, suggesting improved detection methods.

The proposed model achieved a dice coefficient of 0.89, indicating high segmentation accuracy for brain tumors.
Using classical U-Net in this approach supports effective image segmentation processes in medical imaging.
Image processing techniques in pre- and post-processing significantly improved the model's overall performance.
These findings highlight the importance of early detection in treating brain tumors, utilizing advanced AI technologies.

Abdalgadir et al. (2025) studied this question.

View Full Paper

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