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Breast cancer remains one of the most prevalent and life-threatening diseases among women worldwide. Early diagnosis of breast cancer is pivotal in improving patient outcomes and survival rates. The earliest signs of nonpalpable breast cancer are calcifications. This paper proposes a deep learning network for breast calcification areas detection based on YOLO with self-attention mechanism. By using Bi-Level Routing Attention (BRA) mechanisms, the model’s performance can be significantly enhanced. Later, the modified Bi-directional Feature Pyramid Network (BiFPN) technique was used. The advanced model architecture is a modification of the YOLOv8 framework. In order to improve the instances detection of breast calcification, we applied several image preprocessing steps. The contrast of each input image was enhanced and standardized, and the images were resized to a fixed resolution. Utilizing k-fold cross-validation, multiple supervised machine learning techniques were compared. The model demonstrated effective performance across various metrics in the task of calcification detection, achieving a precision rate of 99.32%, a recall rate of 85.0% and an F1-score of 91.59% at the IoU threshold of 0.6. Based on these experimental results, the model is shown to reliably detect areas of breast calcification.
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