Frontiers in Neuroinformatics (Nov 2024)
Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique
Abstract
Brain tumor classification is a critical task in medical imaging, as accurate diagnosis directly influences treatment planning and patient outcomes. Traditional methods often fall short in achieving the required precision due to the complex and heterogeneous nature of brain tumors. In this study, we propose an innovative approach to brain tumor multi-classification by leveraging an ensemble learning method that combines advanced deep learning models with an optimal weighting strategy. Our methodology integrates Vision Transformers (ViT) and EfficientNet-V2 models, both renowned for their powerful feature extraction capabilities in medical imaging. This model enhances the feature extraction step by capturing both global and local features, thanks to the combination of different deep learning models with the ViT model. These models are then combined using a weighted ensemble approach, where each model's prediction is assigned a weight. To optimize these weights, we employ a genetic algorithm, which iteratively selects the best weight combinations to maximize classification accuracy. We trained and validated our ensemble model using a well-curated dataset comprising labeled brain MRI images. The model's performance was benchmarked against standalone ViT and EfficientNet-V2 models, as well as other traditional classifiers. The ensemble approach achieved a notable improvement in classification accuracy, precision, recall, and F1-score compared to individual models. Specifically, our model attained an accuracy rate of 95%, significantly outperforming existing methods. This study underscores the potential of combining advanced deep learning models with a genetic algorithm-optimized weighting strategy to tackle complex medical classification tasks. The enhanced diagnostic precision offered by our ensemble model can lead to better-informed clinical decisions, ultimately improving patient outcomes. Furthermore, our approach can be generalized to other medical imaging classification problems, paving the way for broader applications of AI in healthcare. This advancement in brain tumor classification contributes valuable insights to the field of medical AI, supporting the ongoing efforts to integrate advanced computational tools in clinical practice.
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