Intelligent cervical cancer cell detection method based on deep learning

A technology of cervical cancer cells and deep learning, which is applied to the classification of cell nuclei by deep learning methods, which can solve the problems of long diagnosis time and low diagnosis accuracy of doctors.

Active Publication Date: 2021-02-12
HARBIN UNIV OF SCI & TECH
View PDF8 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problems of low diagnostic accuracy, strong subjectivity, and time-consuming diagnosis of doctors, so as to achieve intelligent, fast and accurate detection of cancer cells

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
  • Intelligent cervical cancer cell detection method based on deep learning
  • Intelligent cervical cancer cell detection method based on deep learning
  • Intelligent cervical cancer cell detection method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0059] In this embodiment, the intelligent cervical cancer screening method based on deep learning is specifically implemented according to the following steps:

[0060] This invention is applied to our self-developed "cancer cell automatic detection system", which is composed of PC, fully automatic microscope, full HD camera and software.

[0061] The automatic cancer cell identification part of the system applies the above-mentioned invention "cervical cancer screening method". First, the user first places the prepared glass slide with the cell sample on the stage for scanning, and then Obtain all the cell images of the patient, and then the specific identification steps for identifying the images are as follows:

[0062] Step 1. Read all the images under the patient folder and put them into the image queue to be segmented, then read the segmentation queue and put them into the U-Net segmentation model to obtain information such as the area of ​​the cell nucleus and the circ...

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 an intelligent cervical cancer cell detection method based on deep learning. The invention relates to classification of cell nucleuses by a deep learning method. The inventionaims to solve the problems of low cancer cell detection accuracy, long consumed time and the like in the existing traditional diagnosis mode. In order to solve the problem, the invention provides an intelligent cervical cancer cell screening method based on deep learning. The method comprises the following specific steps: 1, preparing data; 2, carrying out cell nucleus segmentation; 3, carrying out cell nucleus classification; and 4, screening cancer cells. In the cell nucleus classification part, data expansion and category subdivision are carried out on the data by using an active learning method; on the model, ResNeSt is taken as a basic model, doctor diagnosis experience is introduced, and a more accurate model is trained under the combined action of diagnosis index extraction. Experiments show that the accuracy of the cell nucleus classification method is higher than that of an original model, and in addition, the invention further provides a more effective data preparation methodfor expanding data and subdividing categories. The method is applied to the field of medical image classification.

Description

technical field [0001] The invention relates to the classification of cell nuclei by deep learning methods Background technique [0002] Cervical cancer is the second leading killer of women, second only to breast cancer in incidence and mortality. Cancer has the hope of being cured only if it is detected early, and it is incurable after metastasis occurs in the advanced stage. Therefore, early detection, early diagnosis and early treatment are the only way to deal with the outbreak of cancer. The current diagnosis of cervical cancer mainly relies on manual film reading by doctors, which has a heavy workload and a high rate of misdiagnosis, and it is impossible to carry out large-scale screening. With the development of artificial intelligence technology, the intelligent detection method of cervical cancer cells that has emerged in recent years can effectively reduce the workload of doctors and improve the accuracy of diagnosis by automatically taking pathological images a...

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): G06T7/00G06T7/10G06K9/62
CPCG06T7/0012G06T7/10G06T2207/10056G06T2207/20081G06F18/24
Inventor 何勇军邵慧丽陈德运
Owner HARBIN UNIV OF SCI & TECH
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