Transformer-inspired training principles based breast cancer prediction: combining EfficientNetB0 and ResNet50

Breast cancer is a leading killer and has been deepened by COVID-19, which affected diagnosis and treatment services. The absence of a rapid, efficient, accurate diagnostic tool remains a pressing issue for this severe disease. Thus, it is still possible to encounter issues concerning diagnostic accuracy and utilization of errors in the sphere of machine learning, deep learning, and transfer learning models. This paper presents a new model combining EfficientNetB0 and ResNet50 to improve the classification of breast histopathology images into IDC and non-IDC classes. The implementation steps, it include resizing all the images to be of a standard size of 128*128 pixels and then performing normalization to enhance the learning model. EfficientNetB0 is selected for its efficient yet effective performance while ResNet50 employs deep residual connections to overcome the vanishing gradient problem. The proposed model that incorporates some of the characteristics from both architectures turns out to be very resilient and accurate in classification. The model demonstrates superior performance with an accuracy of 94%, a Mean Absolute Error (MAE) of 0.0628, and a Matthews Correlation Coefficient (MCC) of 0.8690. These results outperform previous baselines and show that the model performs well in achieving a good trade-off between precision and recall. The comparison with the related works demonstrates the superiority of the proposed ensemble approach in terms of accuracy and complexity, which makes it efficient for practical breast cancer diagnosis and screening.