基于IR-VGG的多分类皮肤病实时诊断

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

恶性的皮肤病变在早期阶段的治愈率极高,基于深度学习的皮肤病诊断研究近年来受到持续关注,其诊断准确率较高,然而计算资源消耗大,且依赖于医院大型计算设备。为在物联网移动设备上实现快速准确皮肤病诊断,提出一种基于IR-VGG(inverted residual visual geometry group)的多分类皮肤病实时诊断系统,使用轮廓检测算法分割出皮肤病图像病灶区域,并用反转残差块替换VGG16第一层卷积块以降低网络参数权重和内存开销;将原图像和分割后的病灶图像输入IR-VGG网络,通过全局和局部特征提取后,输出皮肤病诊断结果。实验结果表明,IR-VGG网络结构在SkinData-1和SkinData-2皮肤病数据集上的准确率分别可达到94.71%和85.28%,并且可以有效降低复杂度,使诊断系统较容易在物联网移动设备上进行皮肤病实时诊断。

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.