Automated Brain Tumor Classification using a Fine-Tuned EfficientNet Model

Abstract

Early and accurate brain tumor detection is critical for effective treatment planning and improving patient outcomes. Traditional diagnostic methods, such as biopsies, are invasive and can delay timely intervention. This research proposes an automated, non-invasive approach for brain tumor classification using Magnetic Resonance Imaging (MRI) and a deep learning model based on transfer learning with EfficientNetB0. We curated a comprehensive dataset of 3264 MRI scans, encompassing four distinct categories: glioma, meningioma, pituitary tumors, and healthy brain tissues. Images were acquired in various planes (sagittal, axial, and coronal) and pre-processed to ensure consistency and enhance model performance. Data augmentation techniques, including rotation and resizing, were employed to increase the dataset’s diversity and improve the model’s robustness. The EfficientNetB0 architecture, renowned for its computational efficiency and scalability achieved through compound scaling, serves as the foundation of our model. We leveraged transfer learning by utilizing pre-trained weights from ImageNet and fine-tuning the model with a custom classification head comprising a Global Average Pooling layer, a Dropout layer for regularization, and a Dense layer with SoftMax activation for multi class classification. Performance evaluation on a held-out test set demonstrated a remarkable accuracy of approximately 97% in classifying tumor types. Furthermore, we analyzed the model’s performance using precision, recall, F1 score, and a confusion matrix to provide a comprehensive assessment of its diagnostic capabilities. This research highlights the potential of transfer learning with EfficientNetB0 for developing accurate and efficient automated brain tumor classification systems. Our findings suggest that this approach can contribute significantly to improved diagnostic support, potentially reducing the need for invasive procedures and facilitating timely treatment interventions.