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Skin basal cell carcinoma and abalone warm disease recognition method based on deep learning

A technology of basal cell carcinoma and deep learning, which is applied in the field of recognition of skin basal cell carcinoma and Bowen's disease based on deep learning, can solve the problems of microscopic image recognition of histopathological cells, shorten the time of model training, and improve clinical cell tissue The effect of pathological image data reduction

Pending Publication Date: 2021-01-15
中国人民解放军海军军医大学第一附属医院 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, it mainly recognizes images of clinical skin diseases, and does not recognize microscopic images of histopathological cells. In clinical work, histopathological microscopic images are the main basis for dermatological diagnosis and the "gold standard" for skin tumor diagnosis.

Method used

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  • Skin basal cell carcinoma and abalone warm disease recognition method based on deep learning
  • Skin basal cell carcinoma and abalone warm disease recognition method based on deep learning
  • Skin basal cell carcinoma and abalone warm disease recognition method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0040] see figure 1, a skin basal cell carcinoma and Bowen's disease recognition method based on deep learning, which detects and distinguishes whether the epidermal cells are normal according to the characteristics of the patient's skin histopathological image. The operation steps are as follows:

[0041] 1.1 Obtain skin histocytopathological image samples of skin basal cell carcinoma and Bowen's disease with known diagnosis results, and establish a skin histocytopathological image data set with labels of skin histiocytic diagnosis results;

[0042] 1.2 Preprocessing the skin histocytopathological image in step 1.1, including image enhancement and data enhancement;

[0043] 1.3 Through the trained deep convolutional neural network, image features of skin tissue cells are extracted;

[0044] 1.4 Use the Softmax model to train the pathological image features extracted in step 1.3 to obtain a classification model for skin basal cell carcinoma and Bowen's disease identification;...

Embodiment 2

[0048] This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0049] In this embodiment, the skin histocytopathological image obtained in step 1.1 is cooperated with a professional physician to add the type label of the skin histocytopathological image to ensure that all the labels of the established database are correct.

[0050] In this embodiment, the step 1.2 specifically includes the following steps:

[0051] 1.2.1 For images whose clarity and quality are degraded during the process of collection, dissemination and storage, improve the quality of the image through image enhancement and sharpening;

[0052] 1.2.2 The data enhancement method of flipping, rotating and translating each picture in the data set increases the capacity of the data set;

[0053] 1.2.3 The data set includes three types of samples: normal skin tissue cells, basal cell carcinoma, and Bowen's disease. Due to the inconsistent number of pictures of each type, ...

Embodiment 3

[0059] This embodiment is basically the same as the above-mentioned embodiment, and the special features are as follows:

[0060] figure 1 Based on the operation flowchart of the skin basal cell carcinoma and Bowen's disease recognition system based on deep convolutional neural network, the skin basal cell carcinoma and Bowen's disease recognition method based on deep learning includes the following steps:

[0061] Obtain skin basal cell image samples with known diagnostic result labels, and establish a skin basal cell database with skin basal cell diagnostic result labels;

[0062] Perform image enhancement methods including Gaussian filtering, image smoothing, etc. on the obtained images that are not clear enough, so that the input images maintain a high-quality and clearly distinguishable level;

[0063] According to the number of images, the data enhancement method of flipping and translation is adopted to increase the number of original images, and then the images with l...

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Abstract

The invention provides a skin basal cell carcinoma and abalone warm disease recognition method based on deep learning. According to a recognition system, a large number of skin tissue cell pathological images with labels are used for training the convolutional neural network, and through iterative training, the finally generated classifier can effectively and correctly recognize basal cell carcinoma and abalone warm disease. By modifying the full connection layer and the output layer of the model, rapid and efficient training is achieved, and finally the training result is presented in a software application mode. For dermatologists and pathologists, the invention can help dermatologists and pathologists to effectively reduce heavy workload of recognizing a large number of pathological images and performing diagnosis.

Description

technical field [0001] The invention relates to the field of artificial intelligence recognition of medical images, in particular to a recognition method for skin basal cell carcinoma and Bowen's disease based on deep learning. Background technique [0002] Basal cell carcinoma of the skin and Bowen's disease (also known as squamous cell carcinoma in situ) are two common skin tumors. Clinical practice shows that if these diseases can be diagnosed early, they can usually be cured clinically. Delayed diagnosis will aggravate local tissue damage or greatly increase the risk of metastasis. Traditional diagnostic methods mainly rely on experienced doctors to identify tumor tissue cell characteristics from a large number of tissue cell images under a microscope. Recognizing a large number of pathological images and making correct judgments is a heavy workload, which can easily lead to reduced accuracy of assessment and even lead to misdiagnosis. [0003] In recent years, with th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/08G06N3/04G06K9/62
CPCG06T7/0012G06N3/08G06T2207/20081G06T2207/20084G06T2207/30024G06T2207/30088G06N3/045G06F18/24G06F18/214
Inventor 毕新岭张健滔张晓波陈琢周欣
Owner 中国人民解放军海军军医大学第一附属医院
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