A Secured and Continuously Developing Methodology for Breast Cancer Image Segmentation via U-Net Based Architecture and Distributed Data Training

This research introduces a unique approach to segmenting breast cancer images using a U-Net-based architecture. However, the computational demand for image processing is very high. Therefore, we have conducted this research to build a system that enables image segmentation training with low-power machines. To accomplish this, all data are divided into several segments, each being trained separately. In the case of prediction, the initial output is predicted from each trained model for an input, where the ultimate output is selected based on the pixel-wise majority voting of the expected outputs, which also ensures data privacy. In addition, this kind of distributed training system allows different computers to be used simultaneously. That is how the training process takes comparatively less time than typical training approaches. Even after completing the training, the proposed prediction system allows a newly trained model to be included in the system. Thus, the prediction is consistently more accurate. We evaluated the effectiveness of the ultimate output based on four performance matrices: average pixel accuracy, mean absolute error, average specificity, and average balanced accuracy. The experimental results show that the scores of average pixel accuracy, mean absolute error, average specificity, and average balanced accuracy are 0.9216, 0.0687, 0.9477, and 0.8674, respectively. In addition, the proposed method was compared with four other state-of-the-art models in terms of total training time and usage of computational resources. And it outperformed all of them in these aspects.