Deep Learning-Based CAD System for Enhanced Breast Lesion Classification and Grading Using RFTSDP Approach.

OBJECTIVES: Accurate detection of breast lesion type is crucial for optimizing treatment; however, due to the limited precision of current diagnostic methods, biopsies are often required. To address this limitation, we proposed radio frequency time series dynamic processing (RFTSDP) in 2020, which analyzes the dynamic response of tissue and the impact of scatterer displacement on RF echoes during controlled stimulations to enhance diagnostic information. METHODS: We developed a vibration-generating device and collected ultrafast ultrasound data from 11 ex vivo breast tissue samples under different stimulations. Deep learning (DL) was used for automated feature extraction and lesion classification into 2, 3, and 5 categories. The performance of the convolutional neural network (CNN)-based RFTSDP method was compared with traditional machine learning techniques, which involved spectral and nonlinear feature extraction from RF time series, followed by a support vector machine (SVM). RESULTS: With 65 Hz vibration, the DL-based RFTSDP method achieved 99.53 ± 0.47% accuracy in classifying and grading breast lesions. CNN consistently outperformed SVM, particularly under vibratory stimulation. In 5-class classification, CNN reached 98.01% versus 95.64% for SVM, with the difference being statistically significant (P < .05). Furthermore, the CNN-based RFTSDP method showed a 28.67% improvement in classification accuracy compared to the non-stimulation condition and the analysis of focused raw data. CONCLUSIONS: We developed a DL-based CAD system capable of classifying and grading breast lesions. This study demonstrates that the proposed system not only enhances classification but also ensures increased stability and robustness compared to traditional methods.