Efficient Approaches to Segmentation of Brain Tumors in Medical Images Based on the Vanilla U-Net Deep Learning Model

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30 Dekabr 2025

Main language:Uzbek

Efficient Approaches to Segmentation of Brain Tumors in Medical Images Based on the Vanilla U-Net Deep Learning Model

Field of Science:Artificial IntelligenceComputational Theory and MathematicsComputer Graphics and Computer-Aided DesignComputer Networks and CommunicationsInformation SystemsSignal ProcessingSoftware

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Scientific electronic journal "Digital Economy"

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In this study, the classic Vanilla U-Net deep learning model was used for automatic detection and segmentation of medical images, particularly brain tumors. The model was trained and tested on MRI images. The U-Net architecture consists of encoder and decoder parts, performing segmentation while preserving spatial properties. Experiments used 256x256x3 images, achieving a DICE coefficient of 81% and IoU of 68.19%. In the post-processing stage, segmentation results were compared with original MRI images, confirming the model's effectiveness.

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M.Meliyeva

"SHAROF RASHIDOV NOMIDAGI SAMARQAND DAVLAT UNIVERSITETI" DAVLAT MUASSASASI

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https://eroi.uz/11.142/A026-361644

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References

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Efficient Approaches to Segmentation of Brain Tumors in Medical Images Based on the Vanilla U-Net Deep Learning Model

Scientific electronic journal "Digital Economy"

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