Department of Ultrasound Diagnosis, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou 350007, China.
Department of Hepatobiliary Surgery, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou 350007, China.
J Healthc Eng. 2021 Sep 24;2021:4025597. doi: 10.1155/2021/4025597. eCollection 2021.
Exploring an effective method to manage the complex breast cancer clinical information and selecting a suitable classifier for predictive modeling still require continuous research and verification in the actual clinical environment. This paper combines the ultrasound image feature algorithm to construct a breast cancer classification model. Furthermore, it combines the motion process of the ultrasound probe to accurately connect the ultrasound probe to the breast tumor. Moreover, this paper constructs a hardware and software system structure through machine vision algorithms and intelligent motion algorithms. Furthermore, it combines coordinate transformation and image recognition algorithms to expand the recognition process to realize automatic and intelligent real-time breast cancer diagnosis. In addition, this paper combines machine learning algorithms to process data and obtain an intelligent system model. Finally, this paper designs experiments to verify the intelligent system of this paper. Through experimental research, it can be seen that the breast cancer classification prediction system based on ultrasonic image feature recognition has certain effects.
探索有效的方法来管理复杂的乳腺癌临床信息，并为预测建模选择合适的分类器，仍然需要在实际临床环境中进行持续的研究和验证。本文结合超声图像特征算法构建乳腺癌分类模型。此外，结合超声探头的运动过程，准确地将超声探头连接到乳腺肿瘤上。此外，本文通过机器视觉算法和智能运动算法构建了硬件和软件系统结构。此外，结合坐标变换和图像识别算法，扩展识别过程，实现自动和智能实时乳腺癌诊断。此外，本文结合机器学习算法处理数据并获得智能系统模型。最后，本文设计实验验证本文的智能系统。通过实验研究，可以看出基于超声图像特征识别的乳腺癌分类预测系统具有一定的效果。
