ENSEMBLE METHODS BASED ON CENTERING FOR IMAGE SEGMENTATION

Ensemble methods can be used for many tasks, some of the most popular being: classification, regression, and image segmentation. Image segmentation is a challenging task, where the use of ensemble machine learning methods provides an opportunity to improve the accuracy of neural network predictions. In this study, three new methods for combining neural network predictions were proposed, which were compared with the ensemble averaging method and the conventional use of neural networks. These methods are based on the idea of ​​mask centering and different methods of combining predictions. The main goal of the research is to create more reliable and high-quality ensemble methods that can perform their tasks regardless of image quality. These methods are based on different approaches, which makes it possible to choose a more suitable method for solving a specific problem. Thanks to the use of the proposed methods, a good efficiency of segmentation of medical images on different data was obtained. The obtained results indicate that the proposed methods of combining predictions make it possible to minimize the overall error, better generalize the data and increase the reliability of using predictions. Key words: ensemble methods, deep learning, machine learning, image segmentation.