Convolutional neural network (CNN) based egg embryo classification method

A technology of convolutional neural network and classification method, applied in the field of channel weighting, joint supervision, convolutional neural network, and image processing, it can solve the problems of easy visual fatigue, false detection and missed detection, interference, etc., to avoid the image processing process , high-precision classification, strong adaptability

Inactive Publication Date: 2018-08-24
TIANJIN POLYTECHNIC UNIV
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Problems solved by technology

[0002] At present, in engineering, most of the viability detection and classification of chicken embryos adopt traditional artificial egg detection, and the viability of egg embryos is detected by judging the blood vessels of egg embryos with human eyes, but the method of human eye examination is easily interfered by subjective factors, and There are disadvantages such as visual fatigue and low detection efficiency, which are prone to false detection and missed detection, and it is difficult to meet the high standard requirements of the modern embryo detecti...

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  • Convolutional neural network (CNN) based egg embryo classification method
  • Convolutional neural network (CNN) based egg embryo classification method
  • Convolutional neural network (CNN) based egg embryo classification method

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[0033] The present invention will be further described in detail below in combination with specific embodiments.

[0034] figure 1 A flow chart of the 9-day embryo classification method of the convolutional neural network combined with channel weighting and joint supervision according to the present invention is given. Such as figure 1 As shown, according to the convolutional neural network 9 day embryo classification method of combining channel weighting and joint supervision of the present invention, the method comprises:

[0035] (1) Collect images of egg embryos on day 9 and divide them into two types of samples according to live embryos and dead embryos;

[0036] (2) Preprocessing the embryo image, extracting the ROI region of the image and normalizing the image size;

[0037] (3) Train the target set using a CNN network structure that combines channel weighting and joint supervision;

[0038] (4) Use the trained model to discriminate the image to be tested to verify ...

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Abstract

The invention provides a CNN based egg embryo classification method. The method comprises that egg embryo images in nine days are collected and divided into two types of samples according to the survival state of an embryo; the embryo images are preprocessed, ROI are extracted from the images, and the sizes of the images are normalized; a channel weighting and combined monitoring combined CNN network structure (SJ-CNN) is used to train a target set; and a trained model is used to classify the images to be detected. Compared with a traditional scheme, a complex image processing process is avoided and is not influenced by incomplete image vessel segmentation or violent noise, the method is highly adaptive to a new sample, and high-precision classification can be realized.

Description

technical field [0001] The invention relates to image processing, channel weighting, joint supervision and convolutional neural network, in particular to an embryo classification method. Background technique [0002] At present, in engineering, most of the viability detection and classification of chicken embryos adopt traditional artificial egg detection, and the viability of egg embryos is detected by judging the blood vessels of egg embryos with human eyes, but the method of human eye examination is easily interfered by subjective factors, and There are disadvantages such as visual fatigue and low detection efficiency, which are prone to false detection and missed detection, and it is difficult to meet the high standard requirements of the modern embryo detection and classification industry. Therefore, egg embryo classification detection technology has been extensively studied. [0003] There are many studies on the viability detection and classification of egg embryos us...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06V20/68G06F18/29
Inventor 耿磊王海月肖志涛刘华松王忠强
Owner TIANJIN POLYTECHNIC UNIV
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