We tested the use of a deep learning algorithm to classify the clinical images 
of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, 
intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant 
melanoma, melanocytic nevus, lentigo, pyogenic granuloma, hemangioma, 
dermatofibroma, and wart. The convolutional neural network (Microsoft ResNet-152 
model; Microsoft Research Asia, Beijing, China) was fine-tuned with images from 
the training portion of the Asan dataset, MED-NODE dataset, and atlas site 
images (19,398 images in total). The trained model was validated with the 
testing portion of the Asan, Hallym and Edinburgh datasets. With the Asan 
dataset, the area under the curve for the diagnosis of basal cell carcinoma, 
squamous cell carcinoma, intraepithelial carcinoma, and melanoma was 0.96 ± 
0.01, 0.83 ± 0.01, 0.82 ± 0.02, and 0.96 ± 0.00, respectively. With the 
Edinburgh dataset, the area under the curve for the corresponding diseases was 
0.90 ± 0.01, 0.91 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. With the 
Hallym dataset, the sensitivity for basal cell carcinoma diagnosis was 87.1% ± 
6.0%. The tested algorithm performance with 480 Asan and Edinburgh images was 
comparable to that of 16 dermatologists. To improve the performance of 
convolutional neural network, additional images with a broader range of ages and 
ethnicities should be collected.