Cervical cell image classification method based on convolutional neural network

A convolutional neural network and cervical cell technology, which is applied in the field of cell image processing, can solve the problems of fatigue, high work intensity, low recognition accuracy and low recognition efficiency, and achieves improved feature reuse, accuracy and efficiency. The effect of application value and market prospect

Inactive Publication Date: 2019-10-22
MOTIC XIAMEN MEDICAL DIAGNOSTICS SYST +1
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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

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  • Cervical cell image classification method based on convolutional neural network
  • Cervical cell image classification method based on convolutional neural network
  • Cervical cell image classification method based on convolutional neural network

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[0027] In order to better understand the technical solution of the present invention, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0028] The present invention is a kind of cervical cell image classification method based on convolutional neural network, and this method mainly comprises the following several steps:

[0029] Step 1: Prepare training samples and classify the training samples to obtain eleven types of samples; wherein, the training samples are labeled images of cervical cells, and the eleven types of samples include normal superficial cells, normal middle and bottom cells, granulosa cells, glandular cells, atypical squamous cells, koilocytes, high N / C cells, lymphocytes, clumps, monocytes, and trash.

[0030] Step 2: Build a dense convolutional neural network; including the following sub-steps: input the three-channel cervical cell image with label information into the convolution...

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Abstract

The invention discloses a cervical cell image classification method based on a convolutional neural network. The method comprises the following steps: preparing a training sample, classifying the training sample to obtain eleven types of samples, constructing a convolutional neural network, inputting the training sample into a convolutional neural network model for training, iterating for a certain number of times, stopping training, and storing network weight parameters. When the method is used, a target image is segmented into a to-be-predicted area with cell nucleuses, and then weight parameters and a network structure obtained through training are loaded. The to-be-predicted area is input into the to-be-predicted area. A classification result can be obtained through calculation. The method improves the accuracy and efficiency of cervical cell diagnosis, and has strong adaptive capacity.

Description

technical field [0001] The invention relates to the technical field of cell image processing, in particular to a method for classifying cervical cell images based on a convolutional neural network. Background technique [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. [0003] Convolutional Neural Networks (CNN) is a feed-forward deep neural network that contains convolution operations and is specially designed to process data with a grid-like structure. The convolutional layer and pooling layer contained in the network are the core modules to realize the feature extraction function of the convolutional neural network. The input of the convolution is a two-dimensional featur...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/69G06N3/045G06F18/214
Inventor 史骏代杰
Owner MOTIC XIAMEN MEDICAL DIAGNOSTICS SYST
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