Advancing brain tumor classification: A robust framework using EfficientNetV2 transfer learning and statistical analysis.

Brain tumors are a significant health risk threatening humanity, and they seem to be unique challenges due to their critical location and the complexity of accurate diagnosis and treatment planning. Accurate and timely diagnosis and appropriate treatment planning are crucial for improving health outcomes. However, classifying brain cancer using existing methods often poses a challenge due to either a lack of accuracy, inefficiency, or both. This study proposes a novel approach to brain tumor classification using a CNN based on the EfficientNetV2b0 architecture. This architecture leverages transfer learning, a powerful technique that utilizes pre-trained models on extensive datasets to extract valuable image features. Transferring these learned representations to our task can significantly enhance model performance and reduce training time, overcoming the challenges associated with limited medical image data. Our approach aims to achieve superior classification accuracy, efficiency, and training speed compared to traditional methods. Through efficient preprocessing, data augmentation, and the power of EfficientNetV2, we can achieve a remarkable classification accuracy of 99.16 %, with high precision, recall, and F1 score. Extensive simulations confirmed the robustness of our model, highlighting its potential for clinical application in automated brain tumor classification using MRI scans. Comparative analyses with state-of-the-art CNN architectures, including InceptionResNetV2 and Deep CNN, further validated the system's superior efficacy in accurately categorizing various tumor types. Our findings contribute to advancing the field of brain tumor diagnosis and pave the way for improved patient outcomes.