Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

Brain tumor is a global issue due to which several people suffer, and its early diagnosis can help in the treatment in a more efficient manner. Identifying different types of brain tumors, including gliomas, meningiomas, pituitary tumors, as well as confirming the absence of tumors, poses a significant challenge using MRI images. Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification. These methods often rely on manual feature extraction and basic convolutional neural networks (CNNs). The limitations include inadequate accuracy, poor generalization of new data, and limited ability to manage the high variability in MRI images. Utilizing the EfficientNetB3 architecture, this study presents a groundbreaking approach in the computational engineering domain, enhancing MRI-based brain tumor classification. Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering, showcasing a highly accurate framework with significant potential for healthcare technologies. The model achieves an outstanding 99% accuracy, exhibiting balanced precision, recall, and F1-scores across all tumor types, as detailed in the classification report. This successful implementation demonstrates the model's potential as an essential tool for diagnosing and classifying brain tumors, marking a notable improvement over current methods. The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency, paving the way for wider application. This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.