论文标题
使用LBP和WLD-一种结合方法检测皮肤疾病
Skin Diseases Detection using LBP and WLD- An Ensembling Approach
论文作者
论文摘要
在世界上所有发展中国家和发达国家中，对于所有年龄段的人类来说，皮肤疾病正成为非常频繁的健康问题。皮肤问题影响心理健康，发展对酒精和药物的成瘾，有时会导致社会隔离。考虑到重要性，我们提出了一种自动技术来检测三种流行的皮肤疾病 - 麻风病，Tinea versicolor和Duriligofrom皮肤病变的图像。提出的技术涉及韦伯本地描述符和局部二进制模式，以表示受影响的皮肤区域的纹理模式。这种合奏技术使用多级支持向量机分类器实现了91.38％的精度，其中从基于重心的不同区域提取了功能。我们还应用了一些流行的深度学习网络，例如Mobilenet，resnet_152，googlenet，densenet_121和resnet_101。使用RESNET_101，我们获得了89％的精度。合奏方法明显优于所有使用的深度学习网络。该成像工具将用于早期皮肤疾病筛查。
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.
