基于分层卷积神经网络的皮肤镜图像分类方法

邵虹; 张鸣坤; 崔文成

doi:10.11959/j.issn.2096-6652.202147

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基于分层卷积神经网络的皮肤镜图像分类方法

学术论文 | 更新时间：2024-06-05

- 基于分层卷积神经网络的皮肤镜图像分类方法
- Classification method of dermoscopic image based on hierarchical convolution neural network
- 智能科学与技术学报 2021年3卷第4期页码：474-481
- 作者机构：
- 作者简介：
  
  [ "邵虹（1974- ），女，博士，沈阳工业大学信息科学与工程学院教授，主要研究方向为图像处理与模式识别、智能信息处理" ]
  [ "张鸣坤（1995- ），男，沈阳工业大学信息科学与工程学院硕士生,主要研究方向为智能信息处理" ]
  [ "崔文成（1973- ），男，沈阳工业大学信息科学与工程学院副教授，主要研究方向为智能信息处理" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202147
  中图分类号： TP391.4
- 网络出版日期：2021-12，
  
  纸质出版日期：2021-12-15
- 稿件说明：
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邵虹, 张鸣坤, 崔文成. 基于分层卷积神经网络的皮肤镜图像分类方法[J]. 智能科学与技术学报, 2021,3(4):474-481.

Hong SHAO, Mingkun ZHANG, Wencheng CUI. Classification method of dermoscopic image based on hierarchical convolution neural network[J]. Chinese journal of intelligent science and technology, 2021, 3(4): 474-481.
邵虹, 张鸣坤, 崔文成. 基于分层卷积神经网络的皮肤镜图像分类方法[J]. 智能科学与技术学报, 2021,3(4):474-481. DOI： 10.11959/j.issn.2096-6652.202147.

Hong SHAO, Mingkun ZHANG, Wencheng CUI. Classification method of dermoscopic image based on hierarchical convolution neural network[J]. Chinese journal of intelligent science and technology, 2021, 3(4): 474-481. DOI： 10.11959/j.issn.2096-6652.202147.

摘要

针对皮肤镜图像数量不充足以及各类疾病之间影像数据不平衡的问题，提出一种融合类加权交叉熵损失函数和分层卷积神经网络的皮肤镜图像分类方法。首先对皮肤镜图像进行色彩恒常化处理，消除环境光源噪声；然后构建基于ResNet 50的分层卷积神经网络，并在迁移学习的基础上分别构建二分类和多分类卷积神经网络模型，根据皮肤镜图像的数量特点设置类加权交叉熵损失函数。实验结果表明，该方法具有较好的分类效果，分类准确率达到了85.94%，与未改进的分类模型ResNet 50相比，测试准确率提高了5.752%。

Abstract

In order to solve the problem of insufficient number of dermoscopic image and the imbalance of image data among various diseases

a classification method of dermoscopic image based on class weighted cross entropy loss function and hierarchical convolution neural network was proposed.Firstly

the dermoscopic image was processed by color constancy to eliminate the ambient light noise.Then

the hierarchical convolution neural network based on ResNet 50 was constructed

and the two classification and multi classification convolution neural network models were constructed respectively

and the class weighted cross entropy loss function was set according to the quantitative characteristics of the dermatoscopic image.The experimental results show that the method achieves good classification effect

and the classification accuracy reaches 85.94%.Compared with the improved classification model ResNet 50

the test accuracy is improved by 5.752%.

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references

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