Image classification method and device based on residual network, equipment and medium

A classification method and image technology, applied in the field of image processing, can solve the problems of inability to classify and classify images, achieve the effects of shortening inspection time, overcoming category imbalance, and reducing burden

Pending Publication Date: 2022-01-04
北京理工大学重庆创新中心 +1
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0006] The purpose of the present invention is to provide an image classification method, device, equipment and medium based on residual network in order to overcome the defects of the prior art, aiming to solve the problem that in the current image classification process based on residual network, the Technical Problems of Image Classification

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  • Image classification method and device based on residual network, equipment and medium
  • Image classification method and device based on residual network, equipment and medium
  • Image classification method and device based on residual network, equipment and medium

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Embodiment 1

[0064] Currently, the clinical diagnosis of diabetic retinopathy (DR) and diabetic macular edema (DME) mainly relies on ophthalmologists examining color fundus images. However, the shortage of professional ophthalmologists and the poor medical environment in remote areas seriously restrict the process of screening and diagnosis, and manual real-time analysis is even more difficult. Therefore, it is necessary to introduce computer-assisted treatment to reduce the burden on ophthalmologists, shorten the inspection time, and reduce the time and labor cost of image screening corresponding to different levels of disease.

[0065] In order to solve this problem, various embodiments of the residual network-based image classification method of the present invention are proposed. The image classification method based on the residual network provided by the present invention performs image classification by constructing an image classification model, assists doctors in diagnosis, reduce...

Embodiment 2

[0090] see Figure 4 , as attached Figure 4 Shown is a structural block diagram of an apparatus for image classification based on a residual network provided by this embodiment.

[0091] The device specifically includes:

[0092] The interface writing module 10 is used to obtain the data set classified according to the data feature, and write the data interface required by the convolutional neural network according to the data feature;

[0093] The label making module 20 is used to make a data set label, and each type of data set with different data characteristics corresponds to a data set label;

[0094] The training set construction module 30 is used to construct a training set and a test set, and divide the data set into several equal parts;

[0095] The model building module 40 is used to build a deep residual unit, build a convolutional neural network, and build an image classification model;

[0096] The model training module 50 is used for network training and net...

Embodiment 3

[0114] This preferred embodiment provides a computer device, and the computer device can implement the steps in any of the embodiments of the residual network-based image classification method provided by the embodiment of the present application, and therefore, can implement the method provided by the embodiment of the present application. The beneficial effects of the image classification method based on the residual network can be seen in the previous embodiments, which will not be repeated here.

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Abstract

The invention discloses an image classification method and device based on a residual network, equipment and a medium, and the method comprises the steps: obtaining a data set classified according to data features, and compiling a data interface needed by a convolutional neural network according to the data features; making data set labels, wherein each data set with different data features corresponds to one data set label; constructing a training set and a test set, and dividing the data set into a plurality of equal parts; constructing a deep residual unit, constructing a convolutional neural network, and establishing an image classification model; setting training parameters and a loss function strategy for the image classification model, and determining a classification result output by the classifier; performing network training and network testing on the image classification model; and inputting a to-be-classified image into the image classification model to obtain a classification result. According to the method, the image classification model is constructed to assist doctors in diagnosis, so that the burden of ophthalmologists is relieved, the examination time is shortened, and the time and labor cost for screening pictures corresponding to different grades of diseases are reduced.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image classification method, device, equipment and medium based on a residual network. Background technique [0002] Image classification is widely used in many fields, and deep learning algorithm is currently the most commonly used automatic image processing method. Convolutional neural network (CNN) is a method of feature extraction in an end-to-end manner in deep learning algorithms. It has been widely used not only in object detection and semantic segmentation, but also in the medical field. The residual network can effectively solve the problem of gradient disappearance as the network depth deepens. [0003] Taking the medical field as an example, diabetic retinopathy (DR), a complication of multiple long-duration diabetes, is the most common cause of blindness and visual disability in the world's working-age population. Elevated blood glucose level...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/2415
Inventor 许廷发肖予泽李佳男黄诗淇沈宁
Owner 北京理工大学重庆创新中心
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