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

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Abstract

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 publicly available Invasive Ductal Carcinoma (IDC) dataset comprising samples of different sizes. The results obtained from our rigorous experiments provide compelling evidence for our hybrid model’s robustness and high performance. We achieved a higher accuracy rate of 95%, precision of 94.86%, recall of 94.32%, and F1 score of 94.57% compared to state-of-the-art models. We also identified Light Boosting Classifier (LGB) as the most suitable ML model in conjunction with the ResNet50V2 architecture. The results of this research offer significant contributions to breast cancer detection through an innovative approach, comprehensive performance analysis, and dependable assessment. Moreover, it has the potential to assist medical professionals in making informed decisions, improving patient care, and enhancing outcomes for breast cancer patients.

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