  Objective: Breast cancer screening is of great significance in contemporary
women's health prevention. The existing machines embedded in the AI system do
not reach the accuracy that clinicians hope. How to make intelligent systems
more reliable is a common problem. Methods: 1) Ultrasound image
super-resolution: the SRGAN super-resolution network reduces the unclearness of
ultrasound images caused by the device itself and improves the accuracy and
generalization of the detection model. 2) In response to the needs of medical
images, we have improved the YOLOv4 and the CenterNet models. 3) Multi-AI
model: based on the respective advantages of different AI models, we employ two
AI models to determine clinical resuls cross validation. And we accept the same
results and refuses others. Results: 1) With the help of the super-resolution
model, the YOLOv4 model and the CenterNet model both increased the mAP score by
9.6% and 13.8%. 2) Two methods for transforming the target model into a
classification model are proposed. And the unified output is in a specified
format to facilitate the call of the molti-AI model. 3) In the classification
evaluation experiment, concatenated by the YOLOv4 model (sensitivity 57.73%,
specificity 90.08%) and the CenterNet model (sensitivity 62.64%, specificity
92.54%), the multi-AI model will refuse to make judgments on 23.55% of the
input data. Correspondingly, the performance has been greatly improved to
95.91% for the sensitivity and 96.02% for the specificity. Conclusion: Our work
makes the AI model more reliable in medical image diagnosis. Significance: 1)
The proposed method makes the target detection model more suitable for
diagnosing breast ultrasound images. 2) It provides a new idea for artificial
intelligence in medical diagnosis, which can more conveniently introduce target
detection models from other fields to serve medical lesion screening.
