Deep Feature Fusion and Ensemble Voting Based Brain Tumor Classification Framework
DOI:
https://doi.org/10.5281/zenodo.20412176Keywords:
Brain Tumor, Computer Vision, Image Classification, Deep learning, Biomedical ApplicationAbstract
The classification of brain tumors is important in the diagnosis and treatment planning and patient management. With the increasing need for automated Computer-Aided Diagnosis (CAD) systems, Deep Learning (DL) techniques for biomedical image analysis have gained a more significant pace. In this study, a hybrid deep learning framework is proposed which combines the three most prominent architectures: DenseNet, ResNet, and Xception to classify brain tumors. The advantages of each model are different: Reusing features, residual learning with skip connections and computational efficiency with depth-wise separable convolutions. The proposed ensemble architecture combines the advantages of these architectures and enhances the classification accuracy and robustness. An improved feature selection mechanism is used to refine the deep features extracted from the models and an ensemble deep learning model with maximum voting method is used for final prediction. It is shown that the proposed framework can be effective in accurate and reliable classification of brain tumor through experimental analysis. The results of the experimental analysis show that the proposed framework resulted with an overall classification accuracy of 99.85%, accuracy of 99% and recall of 98%, which is very successful in the brain tumor classification task and can be used to classify the brain tumors accurately and reliably.
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