Overcoming clinical data constraints and class imbalance: enhancing brain tumor diagnosis with deep learning on real-world MRI datasets
This study presents innovative approaches for diagnosing and classifying brain tumors from MRI images using advanced deep learning models to address clinical data constraints — including single-center acquisition, slice-level (not pixel-level) labeling, and real-world imaging variability — alongside extreme class imbalance. By combining convolutional neural networks (CNNs) with recurrent architectures and applying optimization algorithms like Adam and RMSprop, we improve detection accuracy and enhance interpretability. A dataset of 540 MRI images was collected from Hospital, covering various tumor types and patient backgrounds. Preprocessing steps such as normalization, segmentation, and texture-based feature extraction were applied to enhance data quality and model performance. A key innovation of this work is the use of a deep pre-trained model (VGG16), which demonstrates strong generalization potential for future clinical applications. Additionally, our use of real-world hospital data—more challenging than standard Kaggle datasets—makes our results more applicable in practice. Experimental results show that CNN-based models, especially VGG16 and ResNet v2, significantly outperform traditional methods. The VGG16 model achieved a classification accuracy of 97.1%, compared to 85.75% for Random Forest and 83.00% for Support Vector Machine. Crucially, these improvements reveal a critical trade-off: while macro-accuracy reaches 97.1%, Metastatic tumor recall remains at 75% — underscoring that clinical AI must prioritize equitable error distribution over aggregate metrics. Despite these advances, challenges remain, including imbalanced data and preprocessing limitations. Future research should focus on better data balancing and hybrid optimization strategies. This study contributes a robust framework for brain tumor detection, offering practical value for medical imaging and improved patient outcomes.