Breast cancer auxiliary diagnosis method and system based on electromagnetic characteristic analysis

Through multimodal electromagnetic data processing and feature analysis, a parameter mapping relationship table is constructed, which solves the electromagnetic interference problem in breast cancer diagnosis, realizes rapid and accurate pathological classification, and improves diagnostic efficiency and accuracy.

CN120392056APending Publication Date: 2025-08-01THE 941ST HOSPITAL OF THE CHINESE PEOPLES LIBERATION ARMY JOINT LOGISTICS SUPPORT FORCE
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Patent Information

Application Number
CN202510627262.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-15
Publication Date
2025-08-01

AI Technical Summary

Technical Problem

The existing breast cancer diagnosis methods have significant signal distortions under complex electromagnetic interference, which cannot provide intelligent analysis of the pathological type and differentiation of cancer tissues, and the diagnosis timeliness and accuracy is insufficient.

Method used

By obtaining multimodal electromagnetic data of breast tissue, using the generative adversarial network for data repair, combining principal component analysis, convolutional neural network and support vector machine, a parameter mapping relationship between dielectric constant, conductivity and magnetic permeability is constructed, and the dimensionality reduction of electromagnetic characteristics and spatial feature extraction are realized, and pathological classification is finally carried out.

Benefits of technology

It improves the efficiency and anti-interference ability of breast cancer diagnosis, realizes rapid and accurate pathological classification, and is suitable for auxiliary diagnosis of breast cancer in complex electromagnetic environments.

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Abstract

The invention relates to a breast cancer auxiliary diagnosis method and system based on electromagnetic characteristic analysis. The method comprises the following steps: firstly, acquiring mammary gland tissue multi-mode electromagnetic data containing low-frequency induction, high-frequency transmission and microwave reflection data, then preprocessing the data, and utilizing a generative adversarial network to optimize output parameters of a generator through iterative training so as to restore the data to obtain restored electromagnetic data; an electromagnetic feature set is extracted from the repaired data, dimensionality reduction is carried out by using an algorithm, spatial feature extraction is carried out, and local and global electromagnetic feature vectors are obtained; constructing an electromagnetic characteristic model according to the two types of characteristic vectors, and deducing a parameter mapping relation table among the dielectric constant, the conductivity and the magnetic conductivity; and finally, inputting the data in the table into a support vector machine for classification to obtain a pathological classification result whether the tissue is the cancer tissue or not. The method has remarkable technical advantages in the aspects of diagnosis efficiency, anti-interference capability and feature analysis depth, and a new technical path is provided for rapid and accurate diagnosis of breast cancer.
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