Breast Cancer Classification and Segmentation Using Deep Learning on Ultrasound Images
Doha Saad Dajam, Ayman Qahmash · International Journal of Advanced Computer Science and Applications · 2025
Breast cancer continues to pose a major health challenge for women worldwide, highlighting the critical role of accurate and early detection methods in improving patient outcomes. Ultrasound imaging, a commonly used and non-invasive method, is especially useful for identifying tissue irregularities in younger women or individuals with dense breast tissue. However, accurate interpretation of ultrasound images is challenging due to variability in human analysis and limitations in existing deep learning models, which often struggle with small, imbalanced datasets and lack generalizability compared to models trained on natural images. To tackle these challenges, we introduce a dual deep learning framework that combines image classification and tumor segmentation using breast ultrasound images. The classification component evaluates four models (Custom CNN, VGG16, InceptionV3, and MobileNet) while the segmentation module employs a MobileNet-optimized U-Net architecture for precise boundary localization. We validate our approach using the publicly available BUSI dataset, achieving a 98% classification accuracy with MobileNet and a Dice coefficient of 0.8959 for segmentation, indicating high model reliability and spatial agreement. Our method demonstrates a robust, efficient solution to automate breast cancer detection and localization, with potential to support radiologists in early and accurate diagnosis.