  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.
