Automated Brain Tumor Detection From Magnetic Resonance Images Using Fine-Tuned EfficientNet-B4 Convolutional Neural Network

Abstract

Brain tumors are pathological conditions characterized by abnormal cell growth within the brain, which can disrupt normal brain function and pose significant challenges in diagnosis and treatment. Magnetic Resonance Imaging (MRI) is crucial for identifying these tumors, but manual detection is time-consuming and error-prone. This study proposes a novel approach using deep convolutional neural networks (DCNNs) with EfficientNet-B4 as the base model, fine-tuned with customized layers for brain tumor detection. Specifically, our proposed model achieves an impressive overall accuracy of 99.33% for the detection of brain tumors on the publicly available Brain Tumor Detection 2020 Kaggle dataset. This model also conducted a comprehensive ablation study to evaluate the impact of various components on performance, including layer modifications, changes in batch sizes, optimizers, loss functions, and learning rates. This analysis helped to identify the optimal configuration, further enhancing the model’s robustness and classification accuracy. We ensured the robustness of our model through K-Fold cross-validation and a blind test on an independent dataset. Hyperparameter optimization was conducted using Bayesian Optimization to identify the optimal configuration, further enhancing the model’s performance. Comparative analysis against other deep learning (DL) algorithms showcases the efficacy of our approach, with EfficientNet-B4 surpassing all other models, including its variants such as EfficientNet- B0, B1, B2, B3, B5, B6 and B7. Our experimental results demonstrate the superiority of the fine-tuned EfficientNet-B4 model over other convolutional neural network (CNN) architectures, including VGG19, ResNet50, and ResNet101 in terms of recall/sensitivity, classification accuracy, F1-score, F2-score and area under the curve (AUC). These findings underscore the potential of DL-based approaches, particularly the fine-tuned EfficientNet models, in improving the efficiency and accuracy of brain tumor detection from MRI images. Such innovations can potentially improve clinical decision-making and treatment of patients in neuro-oncology.

Keywords

• Brain tumor
• convolutional neural network (CNN)
• deep learning (DL)
• EfficientNet
• magnetic resonance imaging (MRI)
• transfer learning