Abstract:
"High accuracy and reliability are essential for the diagnosis and classification of brain tumors from
medical imaging data, which is a crucial role in clinical diagnostics. In order to enhance brain
tumor identification and categorization, this research proposes the creation of a unique ensemble
deep learning model that integrates the EfficientNet-B0, DenseNet201, and Xception architectures.
The main goal is to improve the precision and resilience of brain tumor identification and
categorization by utilizing the complementing advantages of these cutting-edge models.
To produce a large and varied training dataset, the development procedure required significant
preprocessing and data augmentation. Transfer learning was used to speed up the training process
and improve the model's feature extraction capabilities by tailoring pre-trained models to the
objective of brain tumor diagnosis. This ensemble technique addresses the inherent complexity
and variability of medical imaging data to increase generality and decrease the possibility of
misclassification.
Quantitative testing demonstrated that the ensemble model achieved an impressive overall
accuracy of 99.50%, significantly outperforming the individual models, each of which achieved
accuracies exceeding 98%. The study highlights the potential of advanced ensemble learning
techniques in medical imaging and sets a new benchmark for brain tumor classification systems.
Future enhancements are proposed to further refine the model and expand its clinical applicability,
including the integration of multi-modal imaging data and the development of explainable AI
methods to ensure transparency in decision-making."
