Breast Tumour Segmentation Using Advanced UNet with Saliency, Channel, and Spatial Attention Models

Abstract

Cancers are getting pretty common these days and in that the second most common cancer in the world after lung cancer is breast cancer. The primary screening techniques for early diagnosis of cancerous nodules in women breast are Ultrasound, Mammography, and MRI analysis. However, accurately identifying and outlining the boundaries of tumor regions remains challenging due to their unpredictable shapes, random variations, and blurry outlines. Currently, the CAD based image analysis systems have gained huge attention in biomedical image processing systems along with Machine Learning (ML) and Deep Learning (DL) methods. The conventional machine learning methods suffer from accuracy related issues therefore deep learning-based schemes have been adopted. Specifically, UNet based approach is widely employed for biomedical image segmentation. This research focuses on development of a UNet based deep learning architecture for breast cancer image analysis and segmentation. The proposed model improves the segmentation accuracy by adding saliency model, channel and spatial attention model to consider the low- and high-level features. These modules help to achieve the clear boundary during segmentation. The performance of proposed model is evaluated by using publicly available mammogram dataset known as DDSM dataset.