DenseNet121 with ghost modules and coordinate attention for breast tumor classification in ultrasound images

Breast cancer is one of the leading causes of cancer-related death among women. Early detection of breast tumors through ultrasound imaging can facilitate timely clinical intervention and improve patient outcomes. This study proposes a novel deep learning architecture, Bio-GhostV2-DenseNet, which uses DenseNet121 as the backbone feature extractor, Ghost modules for computationally efficient feature generation, and Coordinate Attention to capture long-range spatial dependencies while preserving positional information. This design enables the model to learn discriminative features while reducing redundant feature computation. The model was trained and tested on a real-world clinical dataset collected at King Abdullah University Hospital, Jordan University of Science and Technology, Jordan, to classify breast tumors as benign or malignant. The proposed model achieved an accuracy of 94.25%, a recall of 91.79%, and an F1-score of 94.11%, outperforming five established deep learning models (MobileNetV2, Xception, NASNetMobile, InceptionV3, and DenseNet121) in terms of accuracy and F1-score. On the original, non-augmented test set, it maintained an accuracy of 93.67% and a recall of 88.37%. When retrained and evaluated on the publicly available three-class Breast Ultrasound Images (BUSI) dataset, the model achieved an accuracy of 88.46%, indicating that the proposed architecture generalizes across datasets. The findings suggest that combining Coordinate Attention with Ghost-based feature generation may improve breast ultrasound classification performance.