Automated Brain Tumor Classification using a Fine-Tuned EfficientNet Model

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