A hepatocellular carcinoma differentiation level grading method based on machine learning

A technology of hepatocellular carcinoma and machine learning, applied in the field of medical devices, to achieve good classification results

Pending Publication Date: 2019-06-28
THE FIRST AFFILIATED HOSPITAL OF ZHENGZHOU UNIV
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Problems solved by technology

[0003] The purpose of the present invention is to provide a method for grading the differentiation level of hepatocellular carc

Method used

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  • A hepatocellular carcinoma differentiation level grading method based on machine learning
  • A hepatocellular carcinoma differentiation level grading method based on machine learning
  • A hepatocellular carcinoma differentiation level grading method based on machine learning

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Experimental program
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Embodiment Construction

[0032] Such as Figure 1-Figure 4 A method for grading the differentiation level of hepatocellular carcinoma based on machine learning includes the following steps:

[0033] Step 1: Establish the front-end server, pre-processing server and data center, and communicate through the Internet between the front-end server, pre-processing server and data center;

[0034] The front-end server is used to store the MRI images, clinical information and pathological information transmitted by the MRI equipment;

[0035] The pre-processing server provides an operation interface for two radiology experts;

[0036] Step 2: The front-end server transmits the MRI images, clinical information and pathological information to the pre-processing server through the Internet, and the two radiologists divide the MRI images into regions of interest through the pre-processing server to generate two sets of images to be processed;

[0037] The pre-processing server sends two sets of images to be proc...

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Abstract

The invention discloses a hepatocellular carcinoma differentiation level grading method based on machine learning. The method comprises the following steps: establishing a prepositive server, a pre-processing server and a data center, carrying out region-of-interest division on the MRI image; selecting stable features of the feature set through Pearson correlation coefficients; carrying out Redundancy removal of features, adopting a support vector machine to train a classification model, obtaining an optimal model. The technical problem of providing graded image data for hepatocellular carcinoma is solved, and a good classification effect is obtained on image classification of hepatocellular carcinoma through feature engineering such as feature construction, feature selection and feature redundancy removal in combination with an SVM linear classifier.

Description

technical field [0001] The invention belongs to the technical field of medical devices, in particular to a method for grading the differentiation level of hepatocellular carcinoma based on machine learning. Background technique [0002] According to the latest authoritative epidemiological data at home and abroad, around 810,000 liver cancer patients die every year worldwide, which ranks fourth among cancer-related fatal diseases (after lung cancer, colorectal cancer and gastric cancer) , among which the death toll in China has accounted for more than half of the world. According to the statistics of tumor-related registration areas in my country, 8 people will die of hepatocellular carcinoma (HCC) every 10 minutes, which accounts for about 85% of liver cancer types. At present, the disease has been regarded as a A serious crisis concerning the health of the whole population. There are many treatment methods for HCC, mainly including: liver resection, liver transplantation,...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
Inventor 翟运开赵杰石金铭甘富文陈昊天陈保站卢耀恩曹明波
Owner THE FIRST AFFILIATED HOSPITAL OF ZHENGZHOU UNIV
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