Algorithm For Generating Synthetic Mammogram Images Based On The Cyclegan Model
Annotatsiya
Mammogram images play a crucial diagnostic role in the early detection of breast cancer. However, the limited availability of medical data, particularly the scarcity of tumor-related images, remains a significant barrier to training artificial intelligence models. To address this challenge, a generative artificial intelligence approach is proposed— specifically, an algorithm for generating synthetic mammogram images based on the CycleGAN (Cycle-Consistent Generative Adversarial Network) model. The proposed approach enables the generation of new synthetic samples through bidirectional image-to-image translation between two classes of images— healthy and tumor-affected mammograms. CycleGAN can operate on any unpaired image dataset and is capable of producing synthetic images that closely resemble real ones, exhibiting statistically consistent characteristics. The increase in the number of synthetic images enhances the generalization capability of deep learning models and improves the accuracy of medical AI systems. In the experimental phase, the CBIS-DDSM and INbreast datasets were utilized, and the visual quality as well as the statistical realism of the generated synthetic images were evaluated. The results demonstrate that the CycleGAN-based algorithm can serve as an effective auxiliary tool for training diagnostic models in scenarios with limited availability of medical images.