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Interpreting Fine-Grained Dermatological Classification by Deep Learning
AbstractThis paper analyzes a deep learning based classification process for common East Asian dermatological conditions. We have chosen ten common categories based on prevalence. With more than 85% accuracy in our experiments, we have tried to investigate why current models are yet to reach accuracy benchmarks seen in object identification tasks. Our current attempt sheds light on how deep learning based dermoscopic identification and dataset creation could be improved.
Related Material
[pdf][bibtex]@InProceedings{Mishra_2019_CVPR_Workshops,
author = {Mishra, Sourav and Imaizumi, Hideaki and Yamasaki, Toshihiko},
title = {Interpreting Fine-Grained Dermatological Classification by Deep Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}
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