@文章{info:doi/10.2196/10513，作者="Zhang, Youshan and Allem, Jon-Patrick and Unger, Jennifer Beth and Boley Cruz, Tess"，标题=" Instagram上水烟(水烟)的自动识别:使用卷积神经网络和支持向量机分类在特征提取中的应用"，期刊="J Med Internet Res"，年="2018"，月="11"，日="21"，卷="20"，数="11"，页="e10513"，关键词="卷积神经网络;特征提取;图像分类;Instagram;社交媒体;背景:Instagram每天有数百万条帖子，可以用来为公共卫生监测目标和政策提供信息。然而，目前基于图像数据的研究往往依赖于对图像的手工编码，耗时耗力，最终限制了研究范围。目前自动化图像分类的最佳实践(如支持向量机(SVM)、反向传播神经网络和人工神经网络)在准确区分图像内物体的能力方面受到限制。目的:本研究旨在演示如何使用卷积神经网络(CNN)提取图像中的独特特征，以及如何使用支持向量机对图像进行分类。方法:从Instagram上收集水烟或水烟(一种与香烟危害相似的新兴烟草产品)的图像，并用于分析(N=840)。 A CNN was used to extract unique features from images identified to contain waterpipes. An SVM classifier was built to distinguish between images with and without waterpipes. Methods for image classification were then compared to show how a CNN+SVM classifier could improve accuracy. Results: As the number of validated training images increased, the total number of extracted features increased. In addition, as the number of features learned by the SVM classifier increased, the average level of accuracy increased. Overall, 99.5{\%} (418/420) of images classified were correctly identified as either hookah or nonhookah images. This level of accuracy was an improvement over earlier methods that used SVM, CNN, or bag-of-features alone. Conclusions: A CNN extracts more features of images, allowing an SVM classifier to be better informed, resulting in higher accuracy compared with methods that extract fewer features. Future research can use this method to grow the scope of image-based studies. The methods presented here might help detect increases in the popularity of certain tobacco products over time on social media. By taking images of waterpipes from Instagram, we place our methods in a context that can be utilized to inform health researchers analyzing social media to understand user experience with emerging tobacco products and inform public health surveillance targets and policies. ", issn="1438-8871", doi="10.2196/10513", url="//www.mybigtv.com/2018/11/e10513/", url="https://doi.org/10.2196/10513", url="http://www.ncbi.nlm.nih.gov/pubmed/30452385" }