As one of the main causes of cancer-related mortality among women worldwide,
breast cancer requires better diagnostic techniques that can provide
non-invasive, fast, and accurate detection. The World Health Organization
(WHO) has a dedicated cancer agency called the International Agency for
Research on Cancer (IARC), whose mission is to undertake and coordinate
research on cancer causes. Mammography is one of many imaging modalities
that is frequently used to find abnormalities. Although automated breast
mass segmentation in mammography is vital, the uniform sizes and shapes of
tumors make it a difficult process. UNet models have shown a significant
segmentation in the medical images. In this study, we propose a prominent
genetic algorithm (GA) for the generation of UNet models by selecting the
optimal parameters. The experiments involved manually generated
architectures, basic UNet model and an attention based UNet, AUNet model
with different filter sizes. As a result of the manual approach, the AUNet
outperformed the base model and hence the AUNet is considered as the base
model for the GA process. The experiments show that the models evolved using
GA are simple and are of small architecture. The model yielded a better
segmentation of the images and outperformed the manually created UNet
models, with dice scores and Intersection over Union (IoU) scores of 91.6%
and 89.2%, respectively.