AI-Powered Radiomics for Breast Cancer Prognosis and Aggressiveness Assessment via Ultrasound Imaging
Abstract: Breast cancer is one of the leading causes of cancer-related deaths globally, and breast ultrasound (BUS) is a critical modality for early detection. This thesis introduces several advanced deep learning-based approaches aimed at improving the accuracy and robustness of automated tumor segmentation and classification systems for breast ultrasound images. The first contribution is a computer-aided diagnostic (CAD) system consisting of two stages: segmentation and classification. In the segmentation stage, an encoder-decoder network utilizing various loss functions is proposed to segment breast tumors. The classification stage fine-tunes the MobileNetv2 network to distinguish between benign and malignant tumors. Experimental results show that the WideResNet architecture, combined with Binary Cross-Entropy (BCE) and Dice loss functions, achieves superior segmentation results, with a Dice score of 77.32%, and the CAD system attains a classification accuracy of 86%.
