  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.
