A thyroid nodule classification system based on ultrasonic radio frequency signals

By performing deep analysis and neural network processing on ultrasound radio frequency signals, the problems of information loss and low efficiency in traditional methods are solved, and high-precision, automated classification of thyroid nodules is achieved.

CN120046031BActive Publication Date: 2026-01-06Wenling Medical Big Data and Artificial Intelligence Research Institute
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Patent Information

Application Number
CN202411913954.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-24
Publication Date
2026-01-06
Estimated Expiration
2044-12-24

AI Technical Summary

Technical Problem

Traditional two-dimensional grayscale images suffer from information loss when assessing thyroid nodules, and existing methods rely on doctors to manually extract features, which is inefficient and difficult to effectively handle noise interference and complex patterns in high-dimensional RF signals.

Method used

We directly perform deep analysis of ultrasound radiofrequency signals, extract features and classify them using a neural network model, including data acquisition, preprocessing, feature transformation, dynamic weight generation and processing of the self-attention layer, to construct a thyroid nodule classification system.

Benefits of technology

It improves the accuracy and reliability of thyroid nodule classification, reduces manual intervention, and realizes an efficient end-to-end classification process, adapting to the needs of different medical institutions and datasets.

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Abstract

The thyroid nodule classification system based on the ultrasonic radio frequency signal of the application comprises a data acquisition module, a model construction module and a classification module. The data acquisition module is used for collecting the ultrasonic radio frequency signal data of the thyroid nodule, pre-processing the ultrasonic radio frequency signal to form an instance set, and constructing a data set with labels. The model construction module is used for constructing a neural network model for feature classification of the thyroid nodule, and training the neural network model based on the data set; the neural network model comprises a feature conversion layer, a dynamic weight generation layer, a self-attention layer and a classification probability fusion layer. The classification module is used for classifying the features of the thyroid nodule based on the trained neural network model. The application has the advantages that the original ultrasonic radio frequency signal is directly analyzed, thereby effectively avoiding the information loss introduced by image reconstruction, and the key information in the ultrasonic radio frequency signal can be accurately captured and analyzed, realizing high-precision classification of the thyroid nodule.
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