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Sample serum quality identification method and identification equipment based on deep learning

A sample and serum technology, applied in the field of medical testing, can solve problems such as weak anti-interference ability, poor serum quality recognition effect, and poor specificity, and achieve the effect of improving sensitivity and specificity, good anti-interference ability, and improving recognition effect

Pending Publication Date: 2021-11-02
NANFANG HOSPITAL OF SOUTHERN MEDICAL UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] However, when using the traditional image segmentation algorithm combined with the color difference model to identify serum quality, there are problems such as poor specificity and weak anti-interference ability, resulting in poor recognition effect of serum quality

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  • Sample serum quality identification method and identification equipment based on deep learning
  • Sample serum quality identification method and identification equipment based on deep learning
  • Sample serum quality identification method and identification equipment based on deep learning

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Embodiment Construction

[0077] In order to understand the technical solutions provided herein, the technical solution of the present application will be described in detail below with reference to the accompanying drawings.

[0078] The embodiment described in the exemplary embodiments is not meant to all embodiments consistent with the present application. Instead, examples thereof are only an example of a method consistent with some aspects of the present application, as detailed in the appended claims.

[0079] In the present application, some terms in the present application will be explained in the art to facilitate understanding of those skilled in the art.

[0080] The terms used in this application are only for the purposes of describing particular embodiments, not intended to limit the invention. The "one", "one", "one", "one", "" "" and "" "used in the present application and the appended claims are also intended to include many forms unless the context clearly represents other meanings. It sho...

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Abstract

The invention discloses a sample serum quality identification method and identification equipment based on deep learning. The method comprises the following steps: acquiring a preprocessed biochemical sample image data set; constructing a deep convolutional neural network model framework, and performing learning training on the deep convolutional neural network model framework based on the preprocessed biochemical sample image data set to obtain a deep convolutional neural network model; obtaining a to-be-identified biochemical sample image, and inputting the to-be-identified biochemical sample image into the deep convolutional neural network model to obtain the probability of hemolysis, jaundice and lipemia of a first biochemical sample corresponding to the to-be-identified biochemical sample image; and determining the judgment conditions of hemolysis, jaundice and lipemia corresponding to the first biochemical sample based on the probabilities of hemolysis, jaundice and lipemia of the first biochemical sample. According to the method, the sensitivity and specificity of serum quality recognition can be improved, good anti-interference capability can be obtained, and the recognition effect of serum quality can be improved.

Description

Technical field [0001] The present application relates to the field of medical testing, and in particular, to a depth study-based sample serum quality identification method and identification equipment. Background technique [0002] Sample quality difference is one of the main reasons for clinical test errors. The sample is unqualified to account for 60% of the analysis before the analysis, and therefore, it is necessary to highly pay attention to how to identify unqualified samples. [0003] In serum quality identification, it is necessary to highly attach great importance to hemolysis, jaundice, blood lipids such as samples. At present, the visual evaluation of sample quality has been widely used in the clinical laboratory, but due to the effects of environmental and physiological factors (such as Paulthon), there is a large difference in visual findings, which is easy to lead to low accuracy. . At present, some sample pre-processing equipment uses a traditional image division ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06N3/08G06T2207/20081G06T2207/30101G06N3/048G06N3/045G06F18/241
Inventor 杨超郑磊李东玲司徒博
Owner NANFANG HOSPITAL OF SOUTHERN MEDICAL UNIV