基于分层卷积神经网络的皮肤镜图像分类方法

doi:10.11959/j.issn.2096-6652.202147

Abstract

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%.

Key words: ResNet 50, classification of dermoscopic image, hierarchical convolution neural network, over fitting

CLC Number:

TP391.4

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.

Figures/Tables 15

类别	A₁	A₂	A₃	A₄	A₅	???	A_M
概率	$\frac{N_{1}}{N}$	$\frac{N_{2}}{N}$	$\frac{N_{3}}{N}$	$\frac{N_{4}}{N}$	$\frac{N_{5}}{N}$	???	$\frac{N_{M}}{N}$

References 14

[1]	HAY R J , JOHNS N E , WILLIAMS H C ,et al. The global burden of skin disease in 2010:an analysis of the prevalence and impact of skin conditions[J]. Journal of Investigative Dermatology, 2014,134(6): 1527-1534.
[2]	BINDER M , SCHWARZ M , WINKLER A ,et al. Epiluminescence microscopy:a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists[J]. Archives of Dermatology, 1995,131(3): 286-291.
[3]	BALLERINI L , FISHER R B , ALDRIDGE B ,et al. A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions[J]. Color Medical Image Analysis, 2013,10(4): 63-86.
[4]	SCHAEFER G , KRAWCZYK B , CELEBI M E ,et al. An ensemble classification approach for melanoma diagnosis[J]. Memetic Computing, 2014,6(4): 233-240.
[5]	CELEBI M E , KINGRAVI H A , UDDIN B ,et al. A methodological approach to the classification of dermoscopy images[J]. Computerized Medical Imaging and Graphics, 2007,31(6): 362-373.
[6]	SUMITHRA R , SUHIL M , GURU D S . Segmentation and classification of skin lesions for disease diagnosis[J]. Procedia Computer Science, 2015,45: 76-85.
[7]	崔文成, 张鹏霞, 邵虹 . 基于深度可分离卷积网络的皮肤镜图像病灶分割方法[J]. 智能科学与技术学报, 2020,2(4): 385-393.
	CUI W C , ZHANG P X , SHAO H . Dermoscopic image lesion segmentation method based on deep separable convolutional network[J]. Chinese Journal of Intelligent Science and Technology, 2020,2(4): 385-393.
[8]	CODELLA N , CAI J J , ABEDINI M ,et al. Deep learning,sparse coding,and SVM for melanoma recognition in dermoscopy images[C]// Proceedings of the 6th International Workshop on Machine Learning in Medical Imaging.[S.l.:s.n.], 2015: 118-126.
[9]	KAWAHARA J , BEN TAIEB A , HAMARNEH G . Deep features to classify skin lesions[C]// Proceedings of 2016 IEEE 13th International Symposium on Biomedical Imaging. Piscataway:IEEE Press, 2016: 1397-1400.
[10]	POMPONIU V , NEJATI H , CHEUNG N M . Deepmole:deep neural networks for skin mole lesion classification[C]// Proceedings of the 23rd IEEE International Conference on Image Processing. Piscataway:IEEE Press, 2016: 2623-2627.
[11]	ESTEVA A , KUPREL B , NOVOA R A ,et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017,542(7639): 115-11811.
[12]	李航, 余镇, 倪东 ,等. 基于深度残差网络的皮肤镜图像黑色素瘤的识别[J]. 中国生物医学工程学报, 2018,37(3): 274-282.
	LI H , YU Z , NI D ,et al. Identification of melanoma using dermoscopic images based on deep residual networks[J]. Chinese Journal of Biomedical Engineering, 2018,37(3): 274-282.
[13]	田敏 . 基于迁移学习的医学文献内图像多标签分类[D]. 武汉:武汉大学, 2019.
	TIAN M . Image multi label classification within medical literature based on migration learning[D]. Wuhan:Wuhan University, 2019.
[14]	HE K M , ZHANG X Y , REN S Q ,et al. Deep residual learning for image recognition[C]// Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016.

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皮肤镜图像	数量/张	占比
基底细胞癌	514	5.13%
光化性角化病	327	3.27%
良性角化病	1 099	10.97%
皮肤纤维瘤	115	1.15%
黑色素瘤	1 113	11.11%
黑色素细胞痣	6 705	66.95%
血管病变	142	1.42%

名称	参数
处理器	I7-7700HQ
内存	8 GB
显卡	GTX 1050 Ti
实验平台	TensorFlow
实验框架	Keras
语言	Python

epoch	原始数据	色彩恒常化处理过的数据
1	467 s	493 s
2	461 s	323 s
3	461 s	335 s
4	463 s	341 s
5	462 s	326 s
6	462 s	326 s
7	462 s	327 s
8	462 s	325 s
9	462 s	325 s
10	463 s	326 s

epoch	0～10	11～20	21～30
训练准确率	72.185%	78.355%	80.164%
测试准确率	71.419%	77.362%	78.938%

性能指标	计算方式	值
召回率	TP/(TP+FN)	90.27%
精确率	TP/(TP+FP)	92.39%
准确率	(TP+TN)/(TP+FP+FN+TN)	85.94%

Classification method of dermoscopic image based on hierarchical convolution neural network

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