Multi-label classification of gynecological secretion images

A classification method and secretion technology, applied in the medical field, can solve the problems of prolonging the classification time, not applicable to the classification of gynecological secretion cell images, etc., and achieve the effect of saving classification time and improving accuracy.

Active Publication Date: 2018-12-28
SHENZHEN UNIV
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Problems solved by technology

The most commonly used method now is manual microscopy, and the existing non-manual methods on the market cannot classify such multi-label images, and can only identify and classify one of the

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  • Multi-label classification of gynecological secretion images
  • Multi-label classification of gynecological secretion images
  • Multi-label classification of gynecological secretion images

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Embodiment Construction

[0057] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0058] Such as figure 1 As shown, the present invention discloses a multi-label gynecological secretion image classification method, comprising the following steps:

[0059] Step 1. Feature extraction

[0060] The feature extraction includes (such as figure 2 shown):

[0061] Step S11, obtaining the original training data, constructing the training database, specifically shooting the original gynecological secretion images ( Figure 6 shown), different types of cells in the gynecological secretion image are labeled separately to generate a label map ( Figure 7 As shown), different types of cells in the label map are represented by different pixel values; the generated label map and the corresponding gynecological secretion image are stored to establish a training database; the gynecological secretion image can be a picture or can be is ...

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Abstract

The invention discloses a multi-label classification method for gynecological secretion images, which comprises the following steps: 1, feature extraction, comprising constructing a training databaseand establishing a network structure for model training; 2, model training, comprising train gynecological secretion images and corresponding label images in a training database through a full convolution network to obtain a required sample model; 3, correcting, testing that gynecological secretion image by a sample model to obtain a predictive label map for testing, evaluating the predictive label map, and judging whether the testing result meets the requirements, if so, taking the sample model as a sample model for predicting; Otherwise, returning to the step 2, modifying each parameter fortraining and repeating the step 3 until the test result meets the requirement; 4, predicting, inputting the gynecological secretion image for detection into a sample model for predicting, and obtaining a preliminary predicted image. Compared with the prior art, the classification time is saved.

Description

technical field [0001] The invention relates to the medical field, in particular to a multi-label classification method for gynecological secretion images. Background technique [0002] At present, when the laboratory department of the hospital conducts gynecological examinations, it is necessary to classify different cells in vaginal secretions, that is, the classification of multi-label images. The most commonly used method now is manual microscopy, and the existing non-manual methods on the market cannot classify such multi-label images, and can only identify and classify one of the cells separately; this will prolong the classification time , and the existing multi-label classification algorithm is not suitable for the classification of gynecological secretion cell images. [0003] How to perform multi-label classification on gynecological secretion images through technical means is a topic worthy of research. Contents of the invention [0004] The purpose of the pre...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/00G06N3/04
CPCG06V20/698G06N3/045G06F18/214G06F18/24
Inventor 李乔亮吴亚杰陈齐文唐洪浩余志刚何旭东齐素文
Owner SHENZHEN UNIV
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