Ultrasonic Image Processing for the Classification of Benign and Malignant Breast Tumors: Comparative Study of Convolutional Neural Network Architectures
Erick Acuña Chambi, Daniel Gil Alzamora, Antonio Angulo · 2025
This study addresses the limitations of conventional breast cancer diagnosis using ultrasound imaging and machine learning. Using KAGGLE data, we applied preprocessing techniques to identify tumour features. VGGNET16 demonstrated 90% accuracy, simplifying tumour classification. Our findings highlight the potential of neural networks to improve diagnosis. By combining ultrasound imaging and machine learning, we offer an accurate and patient-friendly alternative highlighting the urgency of early tumour detection. This research presents an innovative approach with promising results, advancing diagnostic accuracy and patient comfort.