Training method and system of medical image recognition model

By designing the training method of feature extractor and classifier in the medical image recognition model, combined with the noise reduction processing technology, the problems of simple structure and slow convergence speed of BP neural network classifier are solved, and the recognition accuracy and training efficiency of the model are improved.

CN119649101AInactive Publication Date: 2025-03-18XIEHE HOSPITAL ATTACHED TO TONGJI MEDICAL COLLEGE HUAZHONG SCI & TECH UNIV
0 Cites 0 Cited by

Patent Information

Application Number
CN202411691669.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-22
Publication Date
2025-03-18
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The structure of the BP neural network classifier in the convolutional neural network model is too simple, requiring multiple iterations and a large number of training samples. The gradient-decreasing training method causes the model to converge slowly and easily fall into local optimality, affecting the recognition accuracy.

Method used

A training method for medical image recognition models is adopted, including training of feature extractors and classifiers. The feature extractor calculates residuals through forward propagation and backpropagation, updating weights and bias terms until converges. The classifier is designed as a four-layer network, and the predicted value is obtained by calculating the output quotient of the summation layer. At the same time, the training image is denoised, and wavelet transform and hard/soft thresholding are performed using the improved threshold function.

Benefits of technology

It improves the convergence speed and recognition accuracy of the model, reduces training time, enhances classification processing capabilities, and ensures the real-time and accuracy of the model. At the same time, noise reduction processing improves image clarity and detail retention, and enhances the accuracy of model training.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a training method and system for a medical image recognition model, and particularly relates to the technical field of image processing, and the method comprises the following steps: feature extractor training: obtaining a training set sample image, and inputting the training sample image into a network model; calculating an error between the output of the output layer and the target label through forward propagation of the network model; judging whether the network model is converged or not, and if the network model is converged, obtaining a network model trained by the feature extractor; and classifier training: inputting the training image into the feature extractor after feature extraction training is completed. The classifier adopted by the invention starts from the probability density function, the regression value of the dependent variable to the independent variable is directly calculated by using the training sample, iterative operation is not needed, the consumed time is short, the calculation efficiency is ideal, the classification processing capability of the classification model is ensured, the convergence speed of the model training process is high, and the method is suitable for large-scale popularization and application. And the recognition precision, the real-time performance and the accuracy of the recognition model are improved.
Need to check novelty before this filing date? Find Prior Art