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Oral caries image intelligent identification method based on convolutional neural network

A convolutional neural network and intelligent recognition technology, which is applied in the field of artificial intelligence image processing, can solve problems such as missed diagnosis and misdiagnosis, and achieve the effects of reducing difficulty, being easy to use, and improving recognition efficiency

Pending Publication Date: 2021-04-02
福建医科大学附属口腔医院
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Problems solved by technology

[0006] Empirical and human inspection methods that differ from modern intelligent diagnostics mean that diagnoses may be missed and misdiagnosed conditions are even more likely to be detected

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  • Oral caries image intelligent identification method based on convolutional neural network
  • Oral caries image intelligent identification method based on convolutional neural network

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[0031] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0032] It should be pointed out that the following detailed description is exemplary and is intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0033] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components...

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Abstract

The invention relates to an oral caries image intelligent identification method based on a convolutional neural network. According to the method, two image processing modes are used for enhancing feature information, and training is realized by using a deep convolutional neural network through Matlab after early-stage preprocessing. The network used for training is a network in which a large amount of data is learned, and during training, the network is finely adjusted and then trained in a migration training mode. Through parameter judgment obtained in the training process, the network recognition rate obtained by training an image subjected to edge extraction preprocessing is more excellent than that of the other two images. Finally, a corresponding graphical interface is realized by using Matlab, so that tasks such as image loading, preprocessing and regional suggestion, image storage and the like can be completed, and the method is easier to use.

Description

technical field [0001] The invention relates to the fields of artificial intelligence image processing and dental caries detection, in particular to a method for intelligent recognition of oral caries images based on a convolutional neural network. Background technique [0002] Oral caries is the most common oral disease. Incomplete statistics show that currently 40% to 60% of people in China suffer from oral caries, but to varying degrees. Between 50% and 80% of people suffer from periodontal disease. In addition, there are 30%-50% of patients with dental malformation. More than 90% of people over the age of 60 suffer from various oral diseases. Therefore, effective oral disease prevention methods are needed to develop intelligent aided diagnosis of dental caries medical images. [0003] In today's era, people are increasingly pursuing high-function oral care equipment and computer-aided systems. When treating the oral cavity, most of the necessary equipment should be ...

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Application Information

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IPC IPC(8): G06T5/00G06T5/20G06T7/00G06T7/11G06N3/04G06K9/62G06K9/32
CPCG06T5/20G06T7/11G06T7/0012G06T2207/20032G06T2207/20081G06T2207/20084G06T2207/30036G06T2207/30204G06V10/25G06N3/045G06F18/214G06T5/70
Inventor 于皓林秀娇张思慧
Owner 福建医科大学附属口腔医院
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