Breast Cancer classification by adaptive weighted average ensemble of previously trained models
This incremental improvement addresses early detection of breast cancer using histopathology images in CAD systems.
The paper tackled breast cancer classification by proposing an adaptive weighted average ensemble of pre-trained models, which achieved 98% accuracy, a 1% improvement over the best individual model, and reduced false positives and negatives.
Breast cancer is a serious disease that inflicts millions of people each year, and the number of cases is increasing. Early detection is the best way to reduce the impact of the disease. Researchers have developed many techniques to detect breast cancer, including the use of histopathology images in CAD systems. This research proposes a technique that combine already fully trained model using adaptive average ensemble, this is different from the literature which uses average ensemble before training and the average ensemble is trained simultaneously. Our approach is different because it used adaptive average ensemble after training which has increased the performance of evaluation metrics. It averages the outputs of every trained model, and every model will have weight according to its accuracy. The accuracy in the adaptive weighted ensemble model has achieved 98% where the accuracy has increased by 1 percent which is better than the best participating model in the ensemble which was 97%. Also, it decreased the numbers of false positive and false negative and enhanced the performance metrics.