BRAIN TUMOR CLASSIFICATION USING TRANSFER LEARNING WITH MOBILENETV2

This study presents a brain tumor classification system utilizing transfer learning with the MobileNetV2 architecture. Thesystem is designed to classify brain MRI images into four categories: glioma, meningioma, pituitary tumor, and no tumor. The proposed model achieved a test accuracy of 93.36% using a dataset of 7023 MRI images. The results confirm that MobileNetV2, when fine-tuned, offers a computationally efficient yet highly accurate solution suitable for clinical application and edge device deploymen.