A disease intelligent analysis method and system based on ultrasound omics and deep learning

Abstract

The invention discloses an intelligent disease analysis method and system based on ultrasonic omics and deep learning, and the method comprises the steps of obtaining a plurality of pieces of ultrasonic data of a lesion site, and obtaining multi-modal ultrasonic omics data; inputting the multi-modal ultrasound omics data into a deep learning neural network, and adjusting the connection weight, theproportion convolution and the pooling layer of neurons according to the multi-modal ultrasound omics data to obtain adjusted multi-modal ultrasound omics data; and classifying the adjusted multi-modal ultrasonic omics data by using classifiers in different modes, obtaining a score of each classification through a discriminator, and obtaining prognosis judgment, curative effect evaluation and anauxiliary diagnosis result according to the score of each classification. Compared with an existing method for intelligently analyzing diseases by using single-mode ultrasonic data, the technical scheme of the invention optimizes the deep learning network from the aspects of data and model design according to the characteristics of multi-mode ultrasonic omics data, and improves the accuracy and prediction value of intelligent analysis of diseases.

Description

technical field [0001] The present invention relates to the technical field of ultrasonic medicine, in particular to an intelligent disease analysis method and system based on ultrasonic omics and deep learning. Background technique [0002] In the intelligent analysis of diseases, the existing analysis method is to segment, classify and identify medical images by establishing a deep learning model of single-modal medical images, and then manually analyze the processed medical images to obtain the analysis results of diseases . However, when the number of unimodal medical images is limited, more parameters need to be set for training the deep learning model, which will lead to overfitting of the model, and the computational complexity of the deep learning network based on unimodal images is large, and the gradient becomes more and more backward. It is easy to disappear, which may make it impossible to train a usable model, and because ultrasound images have problems such as...

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Problems solved by technology

However, when the number of unimodal medical images is limited, more parameters need to be set for training the deep learning model, which will lead to overfitting of the model, and the computational complexity of the deep learning network based on unimodal images is large, and the gradient becomes more and more backward. It is easy to disappear, which may make it impossible to train a usable model, and because ultrasound images have problems such as large noise, operator dependence, and image standardization, it is impossible to use existing deep learning models to analyze them and obtain auxiliary decision-making analysis of diseases result

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