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.