Brain cancer classification based on multistage ensemble generative adversarial network and convolutional neural network

Abstract

An advanced approach that capitalizes on the synergies between multimodal feature fusion and the dual-path network is presented in this manuscript. Our proposed methodology harnesses a combination of potent techniques, merging the benefits of nonlinear mapping and expansive perception. The foundation of our methodology lies in leveraging well-established pretrained models, namely EfficientNet-B7, ResNet-152, and a meticulously crafted custom convolutional neural network (CNN), to effectively extract salient features from the data. These models are combined in a two-stage ensemble approach. We employ maximum variance unfolding (MVU) to select the most relevant attributes from the extracted features. In this study, we propose a hybrid approach that integrates a generative adversarial network and Neural Autoregressive Distribution Estimation (NADE-K) with a CNN. The resulting two-stage ensemble hybrid CNN model achieves an accuracy of 99.63%. The implementation of the two-stage ensemble hybrid CNN with MVU demonstrates significant improvements in brain tumor classification.