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Classification method of dermoscopic image based on hierarchical convolution neural network
- Chinese Journal of Intelligent Science and Technology Vol. 3, Issue 4, Pages: 474-481(2021)
- 作者机构：
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202147
CLC： TP391.4-
Published Online：2021-12，
Published：15 December 2021
- 稿件说明：
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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.
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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