Convolutional neural network-based cervical cell image recognition method

A convolutional neural network and cervical cell technology, applied in the field of cell image processing, can solve problems such as high work intensity, low recognition accuracy, low recognition efficiency, and fatigue

Inactive Publication Date: 2017-05-31
GUANGXI NORMAL UNIV
View PDF0 Cites 49 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] In my country, the current traditional cervical cell image recognition method is mainly Pap manual film reading technology. Pap manual film reading technology relies on people observing a large number of cell images under a microscope, which is labor-intensive and easy to make people feel tired. The accuracy and recognition efficiency are low

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Convolutional neural network-based cervical cell image recognition method
  • Convolutional neural network-based cervical cell image recognition method
  • Convolutional neural network-based cervical cell image recognition method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0033] refer to image 3 , a cervical cell image recognition method based on convolutional neural network, comprising the steps:

[0034] 1) Prepare training samples:

[0035] (1-1) Read the cervical cell images in the existing gallery as training samples and classify: All the cervical cell images read in are divided into normal cervical cell training samples and diseased cervical cell training samples;

[0036] (1-2) Grayscale: Preprocess the cervical cell image into a grayscale image block, convert the color picture in the cervical cell image into a grayscale image, and then normalize the obtained grayscale image size to 32* 32 grayscale image blocks;

[0037] 2) Build a convolutional neural network layer: build an improved convolutional neural network with adaptive recognition and classification function including adding BN algorithm, the improved convolutional neural network is a multi-layer neural network, and the trainable convolution kernel is used as Filter, filter ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a convolutional neural network-based cervical cell image recognition method. The method is characterized by comprising the following steps of: (1) preparing a training sample; (2) constructing a convolutional neural network layer; (3) constructing a second classifier; and (4) obtaining a recognition result: inputting a to-be-tested cervical cell image into an improved convolutional neural network so as to be automatically recognized and classified by the improved convolutional neural network. The method is high in automation degree and strong is adaptive ability, not only can improve the cervical cell image recognition efficiency, but also can improve the cervical cell image recognition correctness.

Description

technical field [0001] The invention relates to the technical field of cell image processing, in particular to a cervical cell image recognition method based on a convolutional neural network. Background technique [0002] In my country, the traditional method of cervical cell image recognition is mainly based on manual Pap reading technology. The manual Pap reading technology relies on people to observe a large number of cell images under the microscope. The accuracy and recognition efficiency are low. SUMMARY OF THE INVENTION [0003] The purpose of the present invention is to provide a cervical cell image recognition method based on a convolutional neural network in view of the deficiencies of the prior art. [0004] This method has a high degree of automation and strong self-adaptive ability, which can not only improve the efficiency of cervical cell image recognition, but also improve the accuracy of cervical cell image recognition. [0005] The technical scheme that...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/00
CPCG06T7/0012G06T2207/30096G06T2207/20081G06T2207/10004G06V20/695
Inventor 郭磊罗晓曙何富运
Owner GUANGXI NORMAL UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap