使用LBP和WLD的皮肤疾病检测-一种集成方法
Skin Diseases Detection using LBP and WLD- An Ensembling Approach
摘要：
在世界上所有的发展中国家和发达国家，皮肤病正在成为所有年龄组的人类的一个非常常见的健康问题。皮肤问题影响心理健康，对酒精和毒品上瘾，有时还会导致社会孤立。考虑到这一重要性，我们提出了一种从皮肤病变图像中自动检测三种常见皮肤病--麻风病、花斑癣和白癜风的方法。该技术涉及韦伯局部描述符和局部二进制模式来表示受影响的皮肤区域的纹理模式。这种集成技术实现了91.38%的准确率使用多级支持向量机分类器，其中的功能是从不同的区域，是基于重心提取。我们还应用了一些流行的深度学习网络，如MobileNet，ResNet_152，GoogLeNet，DenseNet_121和ResNet_101。我们使用ResNet_101获得了89%的准确率。集成方法明显优于所有使用的深度学习网络。这种成像工具将有助于早期皮肤病筛查。
英文摘要：
In all developing and developed countries in the world, skin diseases are becoming a very frequent health problem for the humans of all age groups. Skin problems affect mental health, develop addiction to alcohol and drugs and sometimes causes social isolation. Considering the importance, we propose an automatic technique to detect three popular skin diseases- Leprosy, Tinea versicolor and Vitiligofrom the images of skin lesions. The proposed technique involves Weber local descriptor and Local binary pattern to represent texture pattern of the affected skin regions. This ensemble technique achieved 91.38% accuracy using multi-level support vector machine classifier, where features are extracted from different regions that are based on center of gravity. We have also applied some popular deep learn-ing networks such as MobileNet, ResNet_152, GoogLeNet,DenseNet_121, and ResNet_101. We get 89% accuracy using ResNet_101. The ensemble approach clearly outperform all of the used deep learning networks. This imaging tool will be useful for early skin disease screening.
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