Rationale and Objectives: To propose a novel deep learning method incorporating multiple regions based on ultrasound and grayscale ultrasound, evaluate its performance in reducing false positives for Breast Imaging Reporting (BI -RADS) category 4 lesions, and compare its diagnostic performance with that of ultrasound experts. Materials and Methods: This study enrolled 163 breast lesions in 161 women from November 2018 to March 2021 n t-enhan ultrasound and conventional ultrasound were performed before surgery or biopsy. A novel deep learning model incorporating multiple regions based on contrast -enhanced ultrasound and grayscale ultrasound was proposed for minimizing the number of false-positive biopsies. The area Linder the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were compared between the deep learning model and ultrasound experts. Results: The AUC, sensitivity, specificity, and accuracy of the deep learning model in BI-RADS category 4 lesions were 0.910 91.5%, 90.5%, and 90.8%, respectively, compared with those of ultrasound experts were 0.869, 89.4%, 84.5%, and 85.9%, respectively. Conclusion: The novel deep learning model we proposed had a diagnostic accuracy comparable to that of ultrasound experts, showing the potential to be clinically useful in minimizing the number of false-positive biopsies.