Method and system for classifying breast tumor images

A breast tumor and classification method technology, applied in the field of image classification, can solve the problems of low accuracy rate of classification method and slow operation speed, etc.

Inactive Publication Date: 2018-11-23
LUDONG UNIVERSITY
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Problems solved by technology

[0005] In order to overcome the problems of low correct rate and slow operation speed of the above-mentioned existing classification methods of breast tumor images, or at least partly solve the above problems, the present invention provides a classification method and system for breast tumor images

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  • Method and system for classifying breast tumor images
  • Method and system for classifying breast tumor images
  • Method and system for classifying breast tumor images

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[0023] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0024] In one embodiment of the present invention, a method for classifying breast tumor images is provided, figure 1 It is a schematic diagram of the overall flow of the method for classifying breast tumor images provided by the embodiment of the present invention, the method includes:

[0025] S101, based on the FCM algorithm, acquiring the mass area in the breast tumor image to be classified;

[0026] Wherein, the mammary gland tumor image to be classified is an ultrasound image of a mammary gland tumor that needs to be classified. Before obtaining the mass region in the breast tumor to be classified based on the FCM algorithm, the image of the breast tumor to be clas...

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Abstract

The invention provides a method and a system for classifying breast tumor images. The method comprises the steps of acquiring lump areas in the to-be-classified breast tumor images on the basis of anFCM algorithm; acquiring mixed characteristics of the lump areas based on a Weka system; and classifying the to-be-classified breast tumor images on the basis of an SVM algorithm according to the mixed characteristics of the lump areas, wherein parameters of the SVM algorithm are optimized by using an improved FOA algorithm in advance. According to the method and the system, the Weka system is used for acquiring the morphology, shape and texture mixed characteristics of the breast tumor lesion areas to perform classification, so that the classification efficiency and accuracy are improved; andin view of the influence of kernel function parameters and penalty coefficients in a support vector machine on the classification performance, parameters of an SVM classifier are optimized by utilizing the improved FOA algorithm, so that the classification performance of the algorithm is improved.

Description

technical field [0001] The invention belongs to the technical field of image classification, and more particularly relates to a method and system for classifying breast tumor images. Background technique [0002] Breast cancer has become one of the cancers with the highest incidence rate among women in the world, but studies have shown that if breast tumors can be detected, diagnosed and treated early, good therapeutic effects can be achieved. Medical research has found that the microscopic images of cell nuclei of breast tumor lesion tissue are different from those of normal tissue, but it is difficult to effectively distinguish them with general image processing methods. Moreover, the output of medical images in medical institutions is huge, and image data often contain a large amount of potential information. [0003] At present, it mainly relies on manual interpretation and analysis of captured images, which has low efficiency and limited information that can be mined, ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08G06T7/11
CPCG06N3/08G06T7/11G06T2207/30096G06F18/213G06F18/23G06F18/2411
Inventor 曲海平李珊珊寇光杰张志旺周春姐贾世祥
Owner LUDONG UNIVERSITY
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