In this paper, machine learning technology for neoplasm segmentation on brain MRI scans is analyzed. This analysis allows to choose the most appropriate machine learning architecture and various preprocessing techniques to increase the precision of tumor instance segmentation. Understanding the image and extracting information from it to accomplish some result is an important area of application in digital image technology. Image segmentation has quickly found its use in medicine, and specifically oncology. Precise segmentation masks may not be critical in other cases, but marginal segmen-tation errors in medical images caused the results to be unreliable in clinical settings. Therefore, biomedical problems require a much higher boundary detection precision to improve further analysis. Comparison of different machine learning algorithms, neural network architectures will achieve the highest accuracy of recognition and segmentation. Python was chosen as the programming language, including Scikit-learn library for basic machine learning algorithms and PyTorch as a deep learning framework.