They began by utilizing the BCI dataset as the cornerstone of their research. Recognizing the potential value of pre-trained models in breast cancer detection and classification, they selected five prominent pre-trained models, including ResNet50, MobileNet, Inception, VGG-SVM, and Desnet, to establish their baseline models. These models serve as a robust foundation upon which they can build and refine their approach.

To further boost the predictive abilities of their models, they employed various ensemble techniques. They experimented with average weighted, soft voting, and hard voting ensembles, meticulously exploring different combinations to identify the optimal approach. By leveraging the collective intelligence of multiple models, they aimed to maximize strengths and compensate for individual limitations. This ensemble-based approach seeks to achieve superior performance compared to individual models. The outputs of their ensemble models, when presented to various machine learning classifiers, demonstrate the potential of their approach in the realm of meta-learning.

In conclusion, the authors' research endeavors to capitalize on the utilization of pre-trained models in breast cancer detection and classification. By integrating ensemble and meta-learning techniques, they aimed to enhance the performance of these models. This approach holds significant promise for the field of breast cancer diagnosis, empowering healthcare professionals to make more informed decisions and facilitating early detection and intervention.

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