Brain tumor classification using a hybrid ensemble of Xception and parallel deep CNN models

Abstract

Objective

Accurate classification of brain tumors is essential for effective diagnosis and treatment planning. The purpose of this study is to develop and evaluate a hybrid ensemble brain tumor classification method to leverage the strengths of two different architectures for improving the accuracy, robustness, and reliability of brain tumor classification.

Methodology

This study introduces a novel and innovative classifier that concatenates the Xception convolutional neural network (CNN) with kernel size of (3,3) and a parallel deep CNN (PDCNN) with kernel size of (5,5) and (12,12) to classify brain tumor images from the Kaggle dataset into four categories: meningioma, glioma, pituitary, and no tumor.

Results

The Xception model alone achieved a classification accuracy of 98.26 %, while the PDCNN model achieved 94.85 % on the same dataset. By concatenating these two models, the proposed hybrid ensemble approach enhanced overall classification accuracy to 99.09 %. In comparison with state-of-the-art models, VGG19 achieved an accuracy of 94.69 %, while ResNet152V2 achieved 96.27 % on the same dataset. The proposed hybrid ensemble model with Xception and PDCNN consistently outperformed both VGG19 and ResNet152V2.

Conclusion

This synergy of concatenating the Xception and PDCNN architectures demonstrates the innovativeness and effectiveness of leveraging complementary strengths in feature extraction and classification, leading to enhanced performance in brain tumor detection. The results highlight the potential of ensemble deep learning models in advancing automated medical image analysis and improving clinical outcomes.