Early Breast Cancer Detection Using an Ensemble Deep Model

Despite great attempts to address them, breast cancer still places a heavy financial and health toll. Asia has received less research than Europe or North America while being under heavy and growing strain from the disease. Due to the possibility of large variations in safety, efficacy, and cost-effectiveness, the lack of region-specific evidence poses a serious danger. This influences the path women to take toward disease identification and management and the availability of resources for putting early detection techniques into action. Early identification is a potentially crucial tactic in reducing the toll of the disease, given the high prognostic benefit of doing so for breast cancer. It consists of two parts: screening and early diagnosis. The majority of the work that has already been done on interventions across the breast cancer continuum of care has come from Western nations. In this study, we describe an ensemble method for categorizing breast cancer histology images. Our proposed ensemble model achieved an accuracy of 92.66%. By making a few further improvements, this algorithm can be further improved to offer a far more sophisticated solution for locating and eliminating cancer cells in the detected area as technology advances in future.