Virtual E Dataset
datasetposted on 25.10.2017 by Seung Seog Han
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
- Virtual E Dataset
E dataset (3317 images) - Diagnosis predicted by CNNs (ResNet-152 + VGG-19; arithmatic mean of both outputs; training dataset: A1)
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).