Deep Learning for Breast Mass Discrimination: Integration of B-Mode Ultrasound & Nakagami Imaging with Automatic Lesion Segmentation

Objective: This study aims to enhance breast cancer diagnosis by developing an automated deep learning framework for real-time, quantitative ultrasound imaging. Breast cancer is the second leading cause of cancer-related deaths among women, and early detection is crucial for improving survival rates. Conventional ultrasound, valued for its non-invasive nature and realtime capability, is limited by qualitative assessments and inter-observer variability. Quantitative ultrasound (QUS) methods, including Nakagami imaging- which models the statistical distribution of backscattered signals and lesion morphology- present an opportunity for more objective analysis. Methods: The proposed framework integrates three convolutional neural networks (CNNs): (1) NakaSynthNet, synthesizing quantitative Nakagami parameter images from B-mode ultrasound; (2) SegmentNet, enabling automated lesion segmentation; and (3) FeatureNet, which combines anatomical and statistical features for classifying lesions as benign or malignant. Training utilized a diverse dataset of 110,247 images, comprising clinical B-mode scans and various simulated examples (fruit, mammographic lesions, digital phantoms). Quantitative performance was evaluated using mean squared error (MSE), structural similarity index (SSIM), segmentation accuracy, sensitivity, specificity, and area under the curve (AUC). Results: NakaSynthNet achieved real-time synthesis at 21 frames/s, with MSE of 0.09% and SSIM of 98%. SegmentNet reached 98.4% accuracy, and FeatureNet delivered 96.7% overall classification accuracy, 93% sensitivity, 98% specificity, and an AUC of 98%. Conclusion: The proposed multi-parametric deep learning pipeline enables accurate, realtime breast cancer diagnosis from ultrasound data using objective quantitative imaging. Significance: This framework advances the clinical utility of ultrasound by reducing subjectivity and providing robust, multi-parametric information for improved breast cancer detection.