Medical image data mining working method through convolutional neural network model

A convolutional neural network and medical imaging technology, applied in the field of image recognition, can solve problems such as disorder, chaos, and low efficiency

Active Publication Date: 2021-07-23
宁波全网云医疗科技股份有限公司
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Problems solved by technology

[0002] Due to the need to collect a large number of medical images for comparison and analysis in the process of medical testing, the efficiency of traditional image recognition methods is low. With the introduction of neural network learning, targeted extraction of massive data has become the focus of image screening and analysis. It is an inevitable trend, but the image data in the existing technology is multivariate, messy, and disordered, which requires a more specific model for classification and extraction, more accurate screening of image contour information, and accurate classification , which urgently requires those skilled in the art to solve the corresponding technical problems

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  • Medical image data mining working method through convolutional neural network model

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[0032] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0033] Such as figure 1 As shown, the present invention discloses a working method for medical imaging data mining through a convolutional neural network model, comprising the following steps:

[0034] S1, obtaining medical image data, preprocessing the medical image, converting it into a Lab color space image; performing noise reduction through Gaussian filtering;

[0035] S2, training and learning the abnormal features in the medical image through the convolutional neural network, and forming a medical image abnormal feature candidate...

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Abstract

The invention provides a medical image data mining working method through a convolutional neural network model, and the method comprises the steps: S1, carrying out the training learning of abnormal features in a medical image through a convolutional neural network, and forming a medical image abnormal feature candidate feature library through an abnormal color screening model; and S2, establishing a feature point weighted local binary description model according to the candidate feature library, and dividing the abnormal features to form medical image division categories of different levels.

Description

technical field [0001] The invention relates to the field of image recognition, in particular to a method for mining medical image data through a convolutional neural network model. Background technique [0002] Due to the need to collect a large number of medical images for comparison and analysis in the process of medical testing, the efficiency of traditional image recognition methods is low. With the introduction of neural network learning, targeted extraction of massive data has become the focus of image screening and analysis. Inevitable trend, but the image data in the prior art is multivariate, messy, and disordered, which requires a more specific model for classification and extraction, more accurate screening of image contour information, and accurate classification , which requires urgently those skilled in the art to solve the corresponding technical problems. Contents of the invention [0003] The present invention aims to at least solve the technical problem...

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Application Information

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IPC IPC(8): G06T7/00G06T5/00G06T7/11G06T7/90G06N3/04
CPCG06T7/0012G06T7/11G06T7/90G06T2207/10024G06T2207/20081G06T2207/20084G06N3/045G06T5/70
Inventor 杨晓凡
Owner 宁波全网云医疗科技股份有限公司
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