MTGA-Net: A Multi-Tier Global-Attention Deep Learning Framework for Enhanced Breast Mass Classification in Mammographic Images
Ala’a R. Al-Shamasneh, Siwar Rekik, Hélène Kanso, Yu Wang · IEEE Access · 2026
Breast cancer, recognized as a leading health threat among women globally, underscores the need for innovations in early detection and diagnostic methods. Traditional mammography, despite being widely used, suffers from time-consuming processes and variability in interpretation. This study aims to enhance the accuracy and efficiency of breast mass classification in mammographic images by developing and validating two innovative Computer-Aided Diagnosis (CAD) systems utilizing deep learning techniques. Robust breast mass grading is crucial for diagnosis, reduces unnecessary biopsies, and assists radiologists in making informed clinical decisions during screening and diagnostic workflows. The study introduces the Multi-tier Global-attention Network (MTGA-Net), designed to enhance feature representation by integrating both local feature extraction and global context information. The system is based on the ResNet152V3 backbone and utilizes global pooling combined with multi-layer perceptron operations to extract both local and global features. MTGA-Net includes a branch-attention module that focuses on critical features of the mass, employing a dual-stage transfer learning approach. Initially, the system leverages the ImageNet dataset for general feature extraction, followed by fine-tuning on a specialized local mass patch dataset. The system’s interpretability and reliability are assessed through Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations, offering insight into model decisions. Empirical validation of MTGA-Net on the Digital Database for Screening Mammography (DDSM) and INbreast dataset showed remarkable performance, achieving an AUC score of 0.98375 and 0.99311, respectively. These findings underscore the transformative potential of AI-driven technologies in medical imaging, establishing a new standard for breast cancer diagnosis and patient care.