Enhancing Brain Tumor Diagnostics with Transfer Learning using EfficientNet

Brain tumors pose severe threats as uncontrolled cell growth in the brain or skull. Their rising death toll necessitates timely and accurate diagnosis, enabling rapid treatment and improved prognosis. While conventional MRI analysis is insufficient, recent advances in computer-aided detection through cerebral imaging techniques have increased brain tumor identification. This study presents a strategy using EfficientNet, a pre-trained deep learning model leveraging transfer learning, to detect brain tumors with high accuracy. Specifically, EfficientNet-B0, B1, and B3 models were evaluated. Impressively, these achieved test accuracies of $98.16 \%, 99.57 \%$, and $82.44 \%$ respectively. The integration of K-Fold crossvalidation further reinforced these results by providing a more reliable estimate of model performance and reducing the risk of overfitting. This rigorous validation process demonstrates the importance of components like resolution calibration, knowledge transfer, and tuning for attaining high classification performance. Initial findings indicate the potential to improve tumor detection sensitivity. Overall, this research shows transfer learning with EfficientNet enables highly effective brain tumor identification, crucial for early diagnosis and intervention. Further optimizations are expected to increase accuracy through model fine-tuning and rigorous methodology. This work significantly advances computer-aided diagnostics for critical neurological disorders.