Hybrid UNET Model Segmentation for an Early Breast Cancer Detection using Ulrasound Images

. In this paper, we present a deep learning framework built on the U-Net architecture. We used a MobileNetV2 and a VGG16 model encoder to handle the semantic segmentation of a biomedical image effectively. This approach is based on the integration of these pre-trained models with the UNet and having an efficient network architecture. By transfer learning, these CNNs are fine-tuned to segment Breast Ultrasound images in normal and tumoral pixels. An extensive experiment of our proposed architecture has been done using Breast Ultrasound Dataset B. Quantitative metrics for evalu?ation of segmentation results including Dice coefficient, Precision, Recall, and , all reached over 80% , which indicates that the method proposed has the capacity to distinguish functional tissues in breast ultrasound images. Thus, our proposed method might have the potential to pro?vide the segmentations necessary to assist the clinical diagnosis of breast cancer and improve imaging in other modes in medical ultrasound.