Segmentation of Breast Cancer on Ultrasound Images using Attention U-Net Model

Breast cancer (BC) is one of the most prevailing and life-threatening types impacting women worldwide. Early detection accurate diagnosis are crucial for effective treatment improved patient outcomes. Deep learning techniques have shown remarkable promise in medical image analysis tasks, particularly segmentation. This research leverages Ultrasound Images BUSI dataset to develop two variations a segmentation model using Attention U-Net architecture. In this study, we trained Attention3 Attention4 U-net on dataset, consisting normal, benign, malignant breast lesions. We evaluated model's performance based standard metrics such as Dice coefficient Intersection over Union (IoU). The results demonstrate effectiveness accurately segmenting lesions, with high overall performance, indicating agreement between predicted ground truth masks. successful application holds improving treatment. It highlights potential deep analysis, paving way more efficient reliable diagnostic tools management.