eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-03-31 18 1 569 580 10.13005/bpj/3109 63607 article Swati Shilaskar 1 Shripad Bhatlawande 1 Mayur Talewar 1 Sidhesh Goud 1 Soham Tak 1 Sachi Kurian 2 Department of E and TC Engineering, Vishwakarma institute of Technology, Pune, India Department of Biomedical Engineering, Rutgers University School of Engineering, New Brunswick, USA Department of E and TC Engineering, Marathwada Mitramandal College of Engineering, Pune, India Breast cancer remains a leading cause of mortality among women worldwide, necessitating accurate and efficient diagnostic methods. This study leverages ultrasound imaging for the early detection of breast tumors, employing the advanced deep learning models: VGG-16 convolutional neural network (CNN) to classify images and the UNet architecture for tumor segmentation. The VGG-16 model, known for extracting high-level features, achieved a classification accuracy of 90%, while UNet reached an impressive 98% accuracy in segmenting tumor regions. The integration of these models provides a robust framework for breast cancer diagnosis, potentially enhancing clinical workflows and facilitating accurate treatment planning. https://biomedpharmajournal.org/vol18no1/classification-and-segmentation-of-breast-tumor-ultrasound-images-using-vgg-16-and-unet/ Breast Tumors Convolutional Neural Networks Deep Learning U-Net VGG-16