Brain Tumor Detection Via Ensemble CNN-Based Deep Learning Models

In recent advancements in cancer diagnosis from MRI imaging, pre-trained deep learning models have shown promise for improving accuracy, efficiency, and scalability. By employing transfer learning, these DL models can leverage their existing knowledge from extensive image-based datasets to adapt to the specific patterns found in MRI scans. In this paper, we compare the performance of MobileNet, ResNet, DenseNet, and EfficientNet models, highlighting the varying strengths of these architectures. We also propose two different ensemble models to improve the accuracy and efficiency of the classification. Extensive experiments on the Br35H brain MRI database demonstrated that these ensemble models exhibit transfer learning capabilities and optimize diagnostic performance. These insights highlight the value of tailored pre-trained models and ensemble methods in enhancing MRI-based cancer diagnostics.