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- Title
Brain cancer classification based on multistage ensemble generative adversarial network and convolutional neural network.
- Authors
Melekoodappattu, Jayesh George; Kandambeth Puthiyapurayil, Chaithanya; Vylala, Anoop; Sahaya Dhas, Anto
- 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. Significance Statement: This study introduces a sophisticated method that combines multimodal feature fusion and dual‐path network, enhancing brain tumor classification. By merging powerful techniques like nonlinear mapping and expansive perception, the authors leverage established models (EfficientNet‐B7, ResNet‐152, and a custom convolutional neural network [CNN]) for extracting key features. Using a two‐stage ensemble approach and maximum variance unfolding to select vital attributes, they propose a hybrid model integrating generative adversarial networks and Neural Autoregressive Distribution Estimation (NADE‐K) with a CNN. Remarkably, this approach achieves an impressive 99.63% accuracy. The results signify a substantial advancement, promising enhanced accuracy in brain tumor diagnosis, marking a significant stride in medical image analysis.
- Subjects
CONVOLUTIONAL neural networks; GENERATIVE adversarial networks; TUMOR classification; BRAIN cancer; CANCER diagnosis
- Publication
Cell Biochemistry & Function, 2023, Vol 41, Issue 8, p1357
- ISSN
0263-6484
- Publication type
Article
- DOI
10.1002/cbf.3870