Fine-Tuned EfficientNetB3 for Enhanced Brain Tumor Detection

Life-threatening diseases like brain tumors call for quick and accurate diagnosis for best treatment. Deep learning has lately showed great potential in automating brain tumor identification from medical imaging. This Study uses the scalability and efficiency of the EfficientNetB3 model, a variation noted for balancing computational cost and performance, for brain tumor categorization. Using a pre-trained EfficientNetB3 model, transfer learning was applied on a sizable annotated MRI brain image dataset to separate between healthy tissue and several tumor kinds. Image preprocessing, augmentation, and optimization strategies were included into the training procedure to improve model generality and accuracy. Accuracy, Precision, Recall, and F1-score among other criteria were used to assess the model's performance. The application of a fine-tuned EfficientNetB3 model in brain tumor detection, which may have not been previously explored by achieving a better trade-off between accuracy and computational efficiency. With an overall accuracy of about 99%, the results show that the EfficientNetB3 model greatly increases the accuracy of brain tumor identification over traditional techniques and other deep learning architectures. The study emphasizes how well EfficientNetB3 might assist radiologists in early, accurate brain tumor diagnosis, hence improving clinical results. This work adds to the mounting data supporting the use of advanced deep learning models in medical imaging.