Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique

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 EcientNet-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 dierent 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 EcientNet-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 oered 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 eorts to integrate
advanced computational tools in clinical pract