A Hybrid Dependable Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detection

Breast cancer is a prevalent and life-threatening disease that requires effective detection and diagnosis methods to improve patient outcomes. Deep learning (DL) and machine learning (ML) techniques have emerged as powerful tools in breast cancer detection, offering benefits such as improved accuracy and efficiency. However, existing methods have scalability and performance limitations, emphasizing the need for further research. In this paper, we propose a hybrid dependable breast cancer detection approach that combines the power of DL using a pre-trained ResNet50V2 model and ensemble-based ML methods. The integration of DL enables the approach to learn and extract hidden patterns from complex breast cancer images, while ML algorithms contribute interpretability and generalization capabilities. We conducted extensive experiments using a breast histopathology image-based…