Skin disease image lesion segmentation method based on deep convolutional neural network

A neural network and deep convolution technology, which is applied in the fields of computer-aided diagnosis and medical image processing, can solve problems such as skin lesion segmentation interference, and achieve the effect of ensuring generalization ability, good edge information, and accurate segmentation results

Pending Publication Date: 2020-12-25
SHENYANG POLYTECHNIC UNIV
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AI Technical Summary

Problems solved by technology

On the one hand, the actual clinically collected images contain a lot of noise information such as hair, blood vessels, and black frames; The huge interference makes the problem of skin disease lesion segmentation a practical and challenging problem

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  • Skin disease image lesion segmentation method based on deep convolutional neural network
  • Skin disease image lesion segmentation method based on deep convolutional neural network
  • Skin disease image lesion segmentation method based on deep convolutional neural network

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Embodiment Construction

[0054] The present invention is described in detail below in conjunction with accompanying drawing:

[0055] The present invention provides a skin disease image lesion segmentation method based on a deep convolutional neural network, which can eliminate noise with high influence in the image, extract rich detail features through the deep convolutional neural network, and greatly improve the accuracy of lesion segmentation. Accuracy. The method includes three steps, and these three steps are to build three modules, which are respectively for data preprocessing, data expansion, and building a segmentation model for training and verification. The data preprocessing module is responsible for the analysis of skin disease images (including clinical images and image) to perform noise reduction processing, remove the artificial and natural noise in the image that hinders the determination of the location of the lesion; the data expansion module is responsible for expanding the data se...

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Abstract

The invention belongs to the field of computer aided diagnosis and medical image processing, and relates to a skin disease image focus segmentation method based on a deep convolutional neural network,which is used for improving the quality of a skin disease image and further improving the focus segmentation accuracy so as to obtain more accurate focus information. The method comprises the specific steps that data preprocessing is responsible for carrying out noise reduction processing on a skin disease image, and removing artificial and natural noise which hinders focus position determinationin the image; the data expansion is responsible for expanding a data set by deforming and rotating the image subjected to the noise reduction processing; a segmentation model is constructed to perform first feature extraction on the image, encoding is performed to obtain more detail features, and the features obtained at the first time are fused to obtain a prediction graph;.

Description

technical field [0001] The invention belongs to the field of computer-aided diagnosis and medical image processing, and in particular relates to a feature extraction and fusion method based on a deep hole convolution network. Background technique [0002] The skin is the largest organ in the human body. It covers and protects the body and performs many important functions such as perspiration, sensation of heat and cold, and pressure. Skin disease is the most common disease infection in humans of all ages, with a high incidence rate. Diseases of human body functions are often directly manifested on skin tissue, which brings a lot of trouble to patients. However, there are many types of skin diseases, more than 2,000 at present, and many skin diseases are extremely similar and difficult to distinguish, which brings great difficulties to disease diagnosis and easily leads to misdiagnosis. As a non-invasive diagnostic technique, dermoscopy can be used to quickly check suspicio...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/10G06T7/174G06T7/187G06T5/00G06T5/30
CPCG06T5/002G06T5/30G06T2207/20081G06T2207/20084G06T2207/20224G06T2207/30088G06T7/10G06T7/174G06T7/187
Inventor 崔文成张鹏霞邵虹
Owner SHENYANG POLYTECHNIC UNIV
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