Skin cancer image recognition method based on attention convolutional neural network

A convolutional neural network and image recognition technology, applied in the field of skin cancer image recognition based on attention convolutional neural network, can solve the problems of inaccurate extraction area and low image recognition rate, saving memory space and simple training process , the effect of improving the accuracy

Active Publication Date: 2019-08-02
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and propose a skin cancer picture recognition method based on atten

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  • Skin cancer image recognition method based on attention convolutional neural network
  • Skin cancer image recognition method based on attention convolutional neural network
  • Skin cancer image recognition method based on attention convolutional neural network

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[0048] The present invention will be further described below in conjunction with specific embodiments.

[0049] Such as figure 1 As shown, the skin cancer image recognition method based on the attention convolutional neural network provided by this embodiment mainly uses data preprocessing, and the attention guides the original network to focus on the local distinctive features to recognize it. The specific situation as follows:

[0050] 1) Image preprocessing

[0051] Use dermoscopy to extract skin cancer images, obtain skin cancer images in the ISIC2017 data set, perform data enhancement on the original images, increase the amount of image data, improve the generalization ability of the model, increase noise data, and improve the robustness of the model. Mainly through image flip, including horizontal flip and vertical flip; image zoom, zoom the picture as a whole; picture rotation, rotate the picture clockwise and counterclockwise by 10 degrees; the picture affine changes, adding...

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Abstract

The invention discloses a skin cancer image recognition method based on an attention convolutional neural network, and the method comprises the steps: 1) carrying out the preprocessing of an image, which comprises the steps of image overturning, rotation, and affine transformation; 2) performing unbalanced sampling on the image, and performing undersampling on the image; 3) establishing a convolutional neural network framework which comprises a convolutional layer, a pooling layer and a full connection layer; 4) establishing an attention mechanism which comprises a channel attention module anda space attention module; and 5) designing an attention convolutional neural network to identify the skin cancer picture, wherein the network added with the attention module can be positioned in a local area more accurately, and images are mined with distinctive characteristics; and 6) performing transfer learning, and initializing parameters of the convolutional neural network by using the parameters of the pre-training network. Through the method of the invention, the accuracy of skin cancer image recognition can be effectively improved, and the development of artificial intelligence in themedical industry is promoted to a certain extent.

Description

technical field [0001] The invention relates to the technical field of image pattern recognition and medical images, in particular to a skin cancer picture recognition method based on attentional convolutional neural network. Background technique [0002] Skin cancer is the most common cancer among all cancers. Skin cancer is usually caused by ultraviolet rays in the sun. Now the death rate of skin cancer is increasing worldwide, and malignant melanoma is the most deadly. There are 71 cases in every 100,000 people, and if detected and treated in time, the success rate of melanoma cure is as high as 98%. In the detection of skin cancer, it is often done using a dermoscopy, a non-invasive skin imaging technique that magnifies an illuminated picture of an area of ​​skin to increase the clarity of spots in the skin by removing reflections from the skin surface , can increase the visual effect of deep skin, and thus can provide more details of skin damage. This approach removes...

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/20081G06T2207/20084G06T2207/30088
Inventor 吴秋霞梁若琳肖丰杨晓伟
Owner SOUTH CHINA UNIV OF TECH
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