AI-based breast cancer detection can improve the sensitivity and specificity of detection, especially for small lesions, which has clinical value in realizing early detection and treatment so as to reduce mortality. The two-stage detection network performs well; however, it adopts an imprecise ROI during classification, which can easily include surrounding tumor tissues. Additionally, fuzzy noise is a significant contributor to false positives. We adopted Faster RCNN as the architecture, introduced ROI aligning to minimize quantization errors and feature pyramid network (FPN) to extract different resolution features, added a bounding box quadratic regression feature map extraction network and three convolutional layers to reduce interference from tumor surrounding information, and extracted more accurate and deeper feature maps. Our approach outperformed Faster R-CNN, Mask R-CNN, and YOLOv9 in breast cancer detection across 485 internal cases. We achieved superior performance in mAP, sensitivity, and false positive rate ((0.752, 0.950, 0.133) vs. (0.711, 0.950, 0.200) vs. (0.718, 0.880, 0.120) vs. (0.658, 0.680, 405)), which represents a 38.5% reduction in false positives compared to manual detection. Additionally, in a public dataset of 220 cases, our model also demonstrated the best performance. It showed improved sensitivity and specificity, effectively assisting doctors in diagnosing cancer.

The strongly supervised deep convolutional neural network (DCNN) has better performance in assessing breast cancer (BC) because of the more accurate features from the slice-level precise labeling compared with the image-level labeling weakly supervised DCNN. However, manual slice-level precise labeling is time consuming and expensive. In addition, the slice-level diagnosis adopted in the DCNN system is incomplete and defective because of the lack of other slices’ information. In this paper, we studied the impact of the region of interest (ROI) and lesion-level multi-slice diagnosis in the DCNN auxiliary diagnosis system. Firstly, we proposed an improved region-growing algorithm to generate slice-level precise ROI. Secondly, we adopted the average weighting method as the lesion-level diagnosis criteria after exploring four different weighting methods. Finally, we proposed our complete system, which combined the densely connected convolutional network (DenseNet) with the slice-level ROI and the average weighting lesion-level diagnosis after evaluating the performance of five DCNNs. The proposed system achieved an AUC of 0.958, an accuracy of 92.5%, a sensitivity of 95.0%, and a specificity of 90.0%. The experimental results showed that our proposed system had a better performance in BC diagnosis because of the more precise ROI and more complete information of multi-slices.