Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images

• This study combines deep transfer learning with U-Net segmentation to revolutionize breast cancer diagnostics. • VGG19 achieved an accuracy (95.5 %) in classifying breast ultrasound images into normal, benign, and malignant categories. • The U-Net model demonstrated high segmentation precision with an average Dice Coefficient of 85.97 %. • Transfer learning models outperformed traditional machine learning and custom CNNs in accuracy and efficiency for breast cancer imaging. • The research underscores AI’s potential to transform diagnostic workflows, enabling earlier and more precise breast cancer detection. Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the need for timely and accurate diagnostic strategies. This study investigates the integration of artificial intelligence (AI) techniques, specifically deep transfer learning for classification and U-Net for segmentation to improve breast cancer diagnosis using ultrasound imaging. A curated dataset of breast ultrasound images, categorized as normal, benign, or malignant, was used for model evaluation. Three pre-trained convolutional neural networks (CNNs), including VGG16, VGG19, and EfficientNet were implemented within a deep transfer learning framework due to their strong feature extraction capabilities. In parallel, the U-Net model, recognized for its effectiveness in medical image segmentation, was employed to delineate tumour boundaries with high spatial precision. Among the CNN models, VGG19 achieved the best performance, with the highest weighted accuracy, precision, and recall. U-Net attained an average Dice Similarity Coefficient of 85.97 %, underscoring its proficiency in segmenting tumour regions across varying lesion types. These AI-based models offer a robust diagnostic pipeline that improves lesion localization, reduces interobserver variability, and supports clinical decision-making. The approach aligns with Sustainable Development Goal (SDG) 3 by promoting early detection and better health outcomes, and SDG 9 through the adoption of innovative AI technologies in healthcare. However, limitations persist, including computational demands, class imbalance, and the lack of dataset diversity, which may affect generalizability. Addressing these challenges is essential for the safe and effective deployment of AI in real-world clinical settings.