Breast cancer is the most prevalent cancer in women globally. More than 2.26 million new cases of breast cancer have been recorded worldwide, and it is a significant cause of cancer-related deaths (World Cancer Research Fund International, 2020). Computer-aided detection has shown promising results in the accurate classification of malignant and benign tumors on breast ultrasound. This study aimed to apply the HoVer-Trans model to the Mayo Clinic breast ultrasound data and to test the model results compared to Jarvey (2022) and Mo et al. (2022). The results showed that the HoVer-Trans model demonstrated greater AUC, which could correctly classify breast lesions as benign or malignant. However, it under-performed in sensitivity compared to a radiologist’s results (Jarvey, 2022). Improved model optimization of the HoVer-Trans model could produce more precise and accurate results than this study’s results. Future research on AI in breast cancer diagnosis using machine learning. Early detection and rapid intervention for breast cancer is essential for patient care and optimal outcomes. Machine learning and AI can contribute significantly to diagnostic medicine in terms of breast cancer