(untitled) (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
Oncological diseases remain one of the most pressing health problems on a global scale. According to data for the last 10 years, the prevalence of cancer has increased by almost 26%, and according to the age-specific standardized morbidity rate (ASIR), by 15%. As overall morbidity rates increase globally, the mortality rate is gradually decreasing. The decrease in cancer-related mortality by 2% is explained by the effectiveness of the use of automated diagnostic processes in early detection and treatment strategies. However, these indicators are very low compared to cases of infection. This indicates the need for the deep application of information technologies in the field of medicine. In particular, it plays an important role in analyzing medical images and optimizing early diagnostic processes using artificial intelligence. In this study, one of the tasks that serves as the basis for the accuracy of automated diagnostics using medical images - the problem of segmentation of medical images - was considered. In this case, the U-Net model based on deep learning was used as an object for the purpose of automatic detection of malignant skin tumors, in particular melanoma, based on dermoscopic images and segmentation of their boundaries, and an accuracy of 92% was achieved.