Finding ways to develop a system to facilitate decision-making in mammography diagnostics using artificial neural networks

Breast cancer is a very common disease. The main principle of successful treatment of this disease is early detection of changes in breast tissue, and the main diagnostic method is mammography. The sensitivity of mammography varies between approximately 70% and 90%, depending on various factors. In recent decades there has been a growing interest in the development and use of mammographic imaging techniques to improve the accuracy of breast cancer diagnosis. This article presents the characteristics of various methods to improve the informativity of mammography.