Breast Cancer Mass Diagnosis System Based on Dynamic Integration Selection Algorithm
By combining the base classifier pool generated by isomorphic and heterogeneous classifiers in the breast cancer diagnosis model and using dynamic capability region algorithm, the problem of low diagnostic accuracy of existing breast cancer diagnosis models is solved, and higher diagnostic accuracy and model stability are achieved.
CN114649090BActive Publication Date: 2025-06-03HEFEI UNIV OF TECH
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Patent Information
- Application Number
- CN202210095136.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-01-26
- Publication Date
- 2025-06-03
- Estimated Expiration
- 2042-01-26
AI Technical Summary
Technical Problem
The existing breast cancer diagnostic models have low diagnostic accuracy, and the dynamic integrated selection algorithm has problems such as limited performance and overly complex decision boundaries when generating classifier pools.
Method used
The base classifier pool is obtained by combining isomorphic classifier generation and heterogeneous classifier generation, and the diagnostic accuracy of breast cancer mass is improved through dynamic capability area algorithm.
Benefits of technology
It improves the accuracy of breast cancer mass diagnosis and enhances the generalization ability and stability of the diagnostic model.
✦ Generated by Eureka AI based on patent content.
Abstract
The present invention provides a breast cancer mass diagnosis system based on a dynamic ensemble selection algorithm, which relates to the field of medical technology. In the present invention, a training module is used to obtain a base classifier pool according to a training set by combining homogeneous classifier generation and heterogeneous classifier generation; a generation module is used to obtain a dynamic ensemble data set corresponding to a test set according to the test set and the base classifier pool; an acquisition module is used to obtain the dynamic ability region of any sample in the dynamic ensemble data set according to a dynamic selection set and the dynamic ensemble data set; a classification module is used to select a preset number of classifiers according to the base classifier pool and the dynamic ability region by using a complementary index, and obtain the diagnostic classification results corresponding to the current sample in the dynamic ensemble data set in each selected classifier. A base classifier pool of the DES algorithm is generated by combining homogeneous classifier generation and heterogeneous classifier generation; and by proposing a dynamic ability region (DROC) algorithm, the diagnostic accuracy of breast cancer masses is improved.
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