Blood cell microscopic image classification method based on regional confusion mechanism neural network

A microscopic image and neural network technology, applied in the field of image recognition and machine learning, can solve problems such as difficulty in achieving convergence, unsatisfactory fine feature extraction, poor classification effect, etc., achieve superior classification performance, improve classification performance, extract accurate effect

Pending Publication Date: 2020-10-30
FUZHOU UNIVERSITY
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The above method is not ideal for the extraction of these fine features, it is

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  • Blood cell microscopic image classification method based on regional confusion mechanism neural network
  • Blood cell microscopic image classification method based on regional confusion mechanism neural network
  • Blood cell microscopic image classification method based on regional confusion mechanism neural network

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[0046] In order to make the features and advantages of this patent more obvious and easy to understand, the following special examples are described in detail as follows:

[0047] like figure 1 As shown, this embodiment provides a method for classifying blood cell microscopic images based on the neural network of the region confusion mechanism, which specifically includes the following steps:

[0048] Step S1: Preprocess the image data, use some conventional data enhancement methods to enrich the image training set, and generalize the model to prevent the model from overfitting and better extract image features.

[0049] Step S2: Construct a deep neural network framework based on the region confusion mechanism, introduce the region confusion mechanism in the training stage, input the processed image data into the training branch in the deep neural network framework, segment the input image, and disrupt the global image structure, and rearrange the sub-regions of the segmented...

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Abstract

The invention provides a blood cell microscopic image classification method based on a regional confusion mechanism neural network. The method comprises the following steps: carrying out preprocessingoperation on a blood cell microscopic image for training; inputting the processed image data into training branches in a deep neural network framework; introducing a regional confusion mechanism in atraining stage; distributing a global structure of an input image, forcing a classifier to extract local fine features of a blood cell image, eliminating the noise interference introduced after confusion through an adversarial learning network, and performing modeling on structure information of the image through semantic correlation between the confused image and an original image; extracting the optimal parameters of the classifier through the branches, directly endowing the optimal parameters to the classification trunk network branches, and carrying out final classification prediction work.

Description

technical field [0001] The invention belongs to the fields of image recognition and machine learning, and in particular relates to a blood cell microscopic image classification method based on a regional confusion mechanism neural network. Background technique [0002] With the development of biomedicine, medical microscopic image technology has been very mature and has been widely used in the medical field. At the same time, with the development of various image processing technologies, there are more and more researches on the processing and analysis of medical images. Blood cells play an important role in human health, have defense and immune functions, and are an important part of the human immune system. When certain characteristics of blood cells change, such as their number and shape, this can be a precursor or symptom of certain diseases. Therefore, analyzing the morphological and quantitative subtypes of various blood cells helps doctors make correct judgments and...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06K9/42G06N3/08
CPCG06N3/08G06V20/69G06V10/32G06F18/24G06F18/214
Inventor 黄捷吴泽钟王武蔡逢煌柴琴琴林琼斌张岳鑫
Owner FUZHOU UNIVERSITY
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