Deep Learning-Based Classification of Brain Tumours on MRI Images Using EfficientNetB0

This study presents an innovative approach to brain tumour classification utilising MRI images and deep learning techniques, specifically the EfficientNetB0 architecture. The methodology involves meticulous dataset curation, comprehensive data preprocessing, and the design of an efficient model architecture. The model achieves a notable accuracy of 98% through rigorous training and evaluation procedures, demonstrating its proficiency in distinguishing between various types of brain tumours, including glioma, meningioma, pituitary tumours, and non-tumour samples. The results reveal commendable precision and recall rates, affirming the model's ability to accurately identify pathological conditions from MRI scans. By employing state-of-the-art deep learning methodologies, this study contributes significantly to the advancement of automated brain tumour diagnostics, offering a reliable tool for clinicians to expedite the diagnosis process and improve patient outcomes. The demonstrated accuracy underscores the potential of deep learning algorithms in enhancing medical imaging analysis, paving the way for more efficient and precise diagnostic systems in clinical practice. Overall, this study underscores the transformative impact of artificial intelligence in medical imaging and highlights the promising prospects for integrating deep learning models into routine healthcare practices for improved patient care and outcomes.