Abstract
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.
Highlights
Breast cancer is one of the high-incidence diseases in women, and its morbidity and mortality account for the first place in female malignant tumors [1]. erefore, accurate diagnosis of breast cancer is of great significance for subsequent treatment
Breast cancer will produce a series of mutations in the continuous division of tumor cells and produce some unknown biochemical reactions, resulting in different biological characteristics from normal cells. erefore, due to the individual specificity of different patients, there will be different changes, and their course of disease development, treatment effect, metastasis status, and recurrence probability are not the same. e above characteristics determine that the diagnosis basis of breast cancer has multiple sources of relevance, the high sequence relevance of the diagnosis and treatment process, and the diversity of postoperative recurrence factors
Evaluation of the Prediction Effect of Breast Classification Based on Ultrasound Image Features e breast cancer detection classification model based on ultrasonic image feature recognition proposed in this study is shown in Figure 7. e model’s overall structure is divided into two modules: lesion location module and lesion fine classification module. e lesion location module is used to detect suspected areas, reduce false negatives, and improve sensitivity. e lesion fine classification module is used to further classify and identify the suspected area and determine whether the suspected area is a lesion
Summary
Breast cancer is one of the high-incidence diseases in women, and its morbidity and mortality account for the first place in female malignant tumors [1]. erefore, accurate diagnosis of breast cancer is of great significance for subsequent treatment. E above characteristics determine that the diagnosis basis of breast cancer has multiple sources of relevance, the high sequence relevance of the diagnosis and treatment process, and the diversity of postoperative recurrence factors. Accurate diagnosis of breast cancer involves many data, wide dimensions, and strong physical and chemical indicators heterogeneity. E consideration dimensions include the choice of examination methods and the diagnosis of breast cancer types, according to the patient’s medical history, physical state, psychology, the affordable economic conditions of the patient’s home, the potential for postoperative recurrence and metastasis, and treatment response prediction and treatment. Erefore, when using artificial intelligence methods to assist breast cancer diagnosis, different examinations are suitable for different models Whether these data with different characteristics should be treated independently or as a complete problem to model is a key. Multifactor features are used as input. ere are more combination possibilities between features and classifiers, such as whether a factor feature should be input into a classifier, or all features should be input as a single feature into a classifier. is further increases the modeling complexity of the problem because different classifiers may classify different information, and people expect to obtain a more reliable model by maximizing the use of this information, rather than choosing the best one from the available classifiers. e decision-making process is further complicated by the absence of established assessment methods for classifier performance, such as repeatability and clinical practicability
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
