基于IR-VGG的多分类皮肤病实时诊断

doi:10.11959/j.issn.2096-3750.2021.00217

Abstract

Abstract:

Malignant skin lesions have a very high cure rate in the early stage.In recent years, dermatological diagnosis research based on deep learning has been continuously promoted, with high diagnostic accuracy.However, computational resource consumption is huge and it relies on large computing equipment in hospitals.In order to realize rapid and accurate diagnosis of skin diseases on Internet of things (IoT) mobile devices, a real-time diagnosis system of multiple categories of skin diseases based on inverted residual visual geometry group (IR-VGG) was proposed.The contour detection algorithm was used to segment the lesion area of skin image.The convolutional block of the first layer of VGG16 was replaced with reverse residual block to reduce the network parameter weight and memory overhead.The original image and the segmented lesion image was inputed into IR-VGG network, and the dermatological diagnosis results after global and local feature extraction were outputed.The experimental results show that the IR-VGG network structure can achieve 94.71% and 85.28% accuracy in Skindata-1 and Skindata-2 skin diseases data sets respectively, and can effectively reduce complexity, making it easier for the diagnostic system to make real-time skin diseases diagnosis on IoT mobile devices.

Key words: skin lesions, edge detection segmentation, inverted residual, deep learning, Internet of things mobile devices

CLC Number:

TP391.41

Ling TAN, Shanshan RONG, Jingming XIA, Sarker SAJIB, Wenjie MA. Real-time diagnosis of multi-category skin diseases based on IR-VGG[J]. Chinese Journal on Internet of Things, 2021, 5(3): 115-125.

Figures/Tables 12

数据集名称	实验名称	准确率	大小	耗时
	实验1	$94 . 71 %$
SkinData-1	实验2	88.97%	5.81 GB	16 591 s
	实验3	92.46%
	实验4	85.02%
	实验1	$85 . 28 %$
SkinData-2	实验2	80.12%	834 MB	4 692 s
	实验3	83.23%
	实验4	79.67%

学习率	准确率	损失率	精确率	召回率	F1分值
0.001	86.77%	0.266 5	89.44%	83.79%	86.52%
0.005	90.13%	0.196 1	90.35%	90.86%	90.61%
0.01	90.75%	0.226 2	87.81%	90.98%	89.36%
$0 . 05$	$94 . 71 %$	$0 . 1237$	$90 . 81 %$	$94 . 70 %$	$92 . 71 %$
0.1	89.76%	0.223 3	85.03%	86.47%	85.74

网络	改进方法	准确率	参数量	损失率
AlexNet	NoneSeg+IR	86.68% 90.43%	29.5 MB4.65 MB	0.273 20.208 9
GoogLeNet	NoneSeg+IR	86.01% 90.10%	6.76 MB6.32 MB	0.140 40.193 8
ResNet34	NoneSeg+IR	84.64% 77.88%	22.8 MB22.7 MB	0.308 70.549 9
ResNet50	NoneSeg+IR	85.84% 63.39%	45.75 MB45.8 MB	0.255 14.744 6
VGG16	None $S e g + I R$	85.02% $94 . 71 %$	138 MB $15 . 18 M B$	0.361 4 $0 . 1237$

已有研究工作	分类模型	准确率	参数量	问题类型
文献[3]所提方案	BN-CNN	89.3%	/	二分类问题
文献[12]所提方案	MSM-CNN	96.3%	很大	多分类问题
文献[15]所提方案	Resnet50	86.7%	46 MB	二分类问题
文献[13]所提方案	Resnet152	86.28%	117 MB	二分类问题
文献[14]所提方案	VGG16（迁移）	91.07%	138 MB	多分类问题
本文模型	$I R - V G G$	$94 . 71 %$	$15 M B$	多分类问题

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皮肤病种类	BCC	BKL	MEL	NV
实例
训练集样本数量	2 127	2 100	2 456	2 696
测试集样本数量	531	524	615	768

Real-time diagnosis of multi-category skin diseases based on IR-VGG

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