An Ensemble Approach: Stacked Pre-Trained Deep Learning Based and Logistic Regression Meta-Learner for Breast Cancer Histopathology Analysis
Rakibur Rahman, Shekh Tanjil Sharif, Md. Hanif Sikder · 2025
Breast cancer is a prominent and fatal cancers that affect adult females worldwide. Improving patient outcomes requires early detection and accurate diagnosis. Recent advances in computer vision and deep learning have yielded encouraging results in analyzing medical images. This study presents the classification of breast cancer photos using ensemble learning techniques with a pre-trained deep learning model and a standard machine learning algorithm as a meta-model to enhance the accuracy of breast cancer determination. This paper demonstrates that the ensemble model outperforms the single deep learning model in terms of classification accuracy and dependable feature extraction, underscoring its greater potential for accurate breast cancer prediction via histopathological images. These results underscore the significance of ensemble learning architectures, such as the proposed model, in enhancing medical image categorization tasks.