segmentation of breast cancer using convolutional neural network and u-net architecture

Breast cancer is a disease of abnormal cell proliferation in the breast tissue organs. one method for diagnosing and screening breast cancer is mammography. however, the results of this mammography image have limitations because it has low contrast and high noise and contrast as non-coherence. this research segmented breast cancer images derived from ultrasonography (usg) photo using a convolutional neural network (cnn) using the u-net architecture. testing on the cnn model with the u-net architecture results the highest mean intersection over union (mean iou) value in the data scenario with a ratio of 70:30, 100 epochs, and a learning rate of 5x10^-5, which is 77%, while the lowest mean iou in the data scenario with a ratio 90:10, 50 epochs, and a learning rate of 1x10^-4 learning rate, which is 64.4%.