Abstract
Breast cancer is the most common non-skin related malignancy and the leading cause of cancer death in women. Mammography remains the gold standard for early detection; however, its accessibility is limited in low-resource settings due to cost and technical complexity. Ultrasound (US) is a viable alternative, but its implementation is hindered by the scarcity of trained radiologists and sonographers. Volume sweep imaging (VSI) has addressed the issue of US acquisition by enabling non-specialists to perform standardized scans. However, these still require expert interpretation, limiting their impact. To overcome this barrier, we propose a fully automated Breast VSI (VSI-B) system integrating artificial intelligence (AI) for segmentation and classification of breast lesions, aiming to provide an accessible diagnostic tool for low-resource environments. This study developed an AI-driven diagnostic system for VSI-B, combining a segmentation model (Attention U-Net 3D) with a classification model for lesion detection. A total of 98 patients with palpable breast lumps were included in the study. The dataset consisted of 392 VSI-B US videos and 2,100 classified frames. A new method was implemented to enhance mass identification by selecting key frames for analysis. A majority voting algorithm was used to optimize lesion classification. The system's performance was assessed based on sensitivity, specificity, and accuracy. Following a detection step that achieved 100% sensitivity and 93.6% specificity for cancer and no cancer patients, as well as 95.0% sensitivity and 63.0% specificity for mass and no mass patients, the DenseNet classification model reached 87% accuracy, 100% sensitivity, and 83% specificity. A majority voting algorithm optimized classification, yielding an AUC of 0.91. This study highlights the potential of an AI-enabled VSI-B system as a reliable diagnostic tool in low-resource settings. By integrating multi-modal segmentation and classification, the system automates breast lesion detection and stratification, reducing reliance on radiologists. The results suggest that this approach could enhance early breast cancer diagnosis and guide clinical decision-making, particularly in underserved regions.