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