Introduction: This study demonstrates the possibility of detecting tumors on mammograms
with high accuracy (more than 72%) using neural networks and studies the characteristics of
machine learning models for improving their efficiency.
Methods: We proposed image preprocessing methods that enable high classification accuracy, as
well as methods of increasing the training set and balancing the distribution of diagnostic classes
when the training set is small. The classification has been done for four diagnostic classes: dysplasia,
pre-cancer state (ductal carcinoma in situ), cancer state (invasive carcinoma), and benign tumor.
Results and Conclusion: We conducted experiments to compare different models based on convolution
neural networks and proposed methods for estimating the model quality. We obtained a base
model that can be used to make recommendations to establish a diagnosis. We studied the characteristics
of the base model and identified promising directions of modification for further improving
the quality estimates.