Multiclass Brain Tumor Detection using EfficientNet B3 Model with Magnetic Resonance Imaging

Deep learning models for brain tumour diseases identification have grown in importance for accurate and efficient medical imaging diagnosis. In this study, the EfficientNetB3 architecture used, a cutting-edge convolutional neural network (CNN) noted for its efficiency and performance, to create a multiclass brain tumour detection system. The goal is to categorise brain MRI scans into four types: no tumour, glioma, meningioma, and pituitary tumour. The effective use of EfficientNetB3 for brain tumour diagnosis demonstrates the utility of advanced deep learning architectures in medical picture processing. EfficientNetB3 can help healthcare practitioners diagnose and treat brain tumours, allowing for more timely treatments and personalised patient care. this study illustrates the revolutionary influence of deep learning in neuro-oncology and paves the door for improved diagnostic capabilities in brain tumour detection.