Automated Brain Tumour Detection from MRI Using Fine Tuned Efficientnet-B0

Abstract-Brain tumour disease arises from the uncontrolled growth of cells. Detecting brain tumours early is crucial for successful treatment. Many current diagnostic methods are cumbersome, demand significant manual input, and yield less-than-ideal results. The EfficientNet-B0 architecture was utilized to diagnose brain tumours through magnetic resonance imaging (MRI). This refined architecture was applied to classify four distinct stages of brain tumours from MRI images. The fine-tuned model achieved 99% accuracy in identifying four different brain tumour classes: glioma, no tumour, meningioma, and pituitary. The proposed model excelled in detecting the pituitary class, with a precision of 0.95, recall of 0.98, and an F1 score of 0.96. It also performed exceptionally well in identifying the no-tumour class, with precision, recall, and F1 score values of 0.99, 0.90, and 0.94, respectively. The precision, recall, and F1 scores for the Glioma and Meningioma classes were also notably high. This proposed solution holds significant potential for improving clinical assessments of brain tumours.