Abstract:
Early and accurate brain tumor detection is critical for effective treatment
planning and improving patient outcomes. Traditional diagnostic methods, such
as biopsies, are invasive and can delay timely intervention. This research
proposes an automated, non-invasive approach for brain tumor classification
using Magnetic Resonance Imaging (MRI) and a deep learning model based on
transfer learning with EfficientNetB0. We curated a comprehensive dataset of
3264 MRI scans, encompassing four distinct categories: glioma, meningioma,
pituitary tumors, and healthy brain tissues. Images were acquired in various
planes (sagittal, axial, and coronal) and pre-processed to ensure consistency
and enhance model performance
