Brain tumor multi classification and segmentation in MRI images using deep learning

Belal Amin, Romario Sameh Samir, Youssef Tarek, Mohammed Ahmed, Rana Ibrahim +2 more
4/20/2023
eess.IVcs.CVcs.LG

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Abstract

This study proposes a deep learning model for the classification and segmentation of brain tumors from magnetic resonance imaging (MRI) scans. The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images. The models are evaluated on a publicly available dataset and achieve high accuracy and segmentation metrics, indicating their potential for clinical use in the diagnosis and treatment of brain tumors.

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Cite this paper

@article{amin2023brain,
  title  = {Brain tumor multi classification and segmentation in MRI images using deep learning},
  author = {Belal Amin and Romario Sameh Samir and Youssef Tarek and Mohammed Ahmed and Rana Ibrahim and Manar Ahmed and Mohamed Hassan},
  year   = {2023},
  eprint = {2304.10039},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.10039v2}
}

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