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

2023-04-20 · Belal Amin, Romario Sameh Samir, Youssef Tarek, Mohammed Ahmed, Rana Ibrahim, Manar Ahmed, Mohamed Hassan

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.

📄 PDF Abstract BibTeX arXiv:2304.10039

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