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September 10, 2025

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

Key Points

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.

Abstract

Brain tumor is a very fatal health problem and unfortunately it is getting more common in modern society. Developing medical methods and technologies make possible to detect the disease earlier, slow down its progress and treat it. Early detection is very crucial for the success of treatment processes. Usage of image processing and artificial intelligence methods can help medics for early detection of the disease. In this study, a deep learning based enhanced image segmentation approach has been proposed to detect brain tumors. Segmentation was performed on the brain magnetic resonance (MR) images which were taken from a public dataset. Classical U-Net structure were employed at segmentation process because of its compatibility and success in medical image segmentation. Performance of the proposed model was increased with the help of image processing techniques used in pre- and post-processing stages. After using some image enhancement techniques as post-processing, a 0.89 of the dice coefficient, a 0.85 of the sensitivity and a 0.89 of the F-score were obtained.

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