针对基于深度学习的乳腺癌诊断中小肿块和互相遮挡肿块易被漏诊的问题，提出了一种用于乳腺肿块检测的改进型YOLOv3算法。首先，在特征融合模块中添加了自底向上的路径，并采用级联和跨层连接的方式充分利用底层特征信息，提高了小肿块的识别精度；其次，为了筛选出更精确的预测框，避免互相遮挡的肿块出现漏检的情况，在软非极大值抑制（Soft-NMS）算法中引入了距离交并比（DIoU）来抑制冗余的预测框。实验结果表明，所提乳腺肿块检测算法在检测小肿块和互相遮挡的肿块方面有较高的准确率和速度，平均均值精度（mAP@0.5）达到了96.1%，相比于YOLOv3提高了1.8个百分点，且每张钼靶图像的检测时间仅为28 ms。

Aiming at the problem that small masses and occluded masses are easy to be missed in breast cancer diagnosis based on deep learning, an improved YOLOv3 algorithm for breast mass detection is proposed. First, a bottom-up path is added into the feature fusion module, and the cascading and cross-layer connections are adopted to make full use of the underlying feature information to improve the recognition accuracy of small masses. Second, to filter out more accurate prediction bounding boxes and avoid missed detection of masses that occlude each other, the distance intersection over union (DIoU) is introduced in soft non-maximum suppression (Soft-NMS) algorithm to suppress the redundant prediction bounding boxes. The experimental results demonstrate that the proposed breast mass detection algorithm has high accuracy and speed in detecting small masses and occluded masses, mean average precision (mAP@0.5) reaches 96.1%, which is 1.8 percentage point higher than that of YOLOv3, and the detection time of each mammogram target image is only 28 ms.