Virtual E Dataset

2017-10-25T18:08:10Z (GMT) by Seung Seog Han
<div>- Virtual E Dataset</div><div><br></div><div>E dataset (3317 images) - Diagnosis predicted by CNNs (ResNet-152 + VGG-19; arithmatic mean of both outputs; training dataset: A1)</div><div><br></div><div>We created the E dataset to assess the semisupervised learning performance by conducting a Web-based image search for “tinea,” “onychomycosis,” “nail dystrophy,” “onycholysis,” and “melanonychia” in English, Korean, and Japanese on http://google.com and http://bing.com, and downloaded a total of 15,844 images. From these images, the R-CNNs created a nail dataset of 3,317 images, since we had to discard many images because of low image resolution. The CNNs (model: ResNet-152 + VGG-19; arithmetic mean of both outputs; training dataset: A1) automatically classified images generated by the R-CNNs into six classes (760 onychomycosis, 1,316 nail dystrophy, 363 onycholysis, 185 melanonychia, 424 normal, and 269 others).</div>