Interpreting Fine-Grained Dermatological Classification by Deep Learning

Sourav Mishra, Hideaki Imaizumi, Toshihiko Yamasaki

CVPRW 2019 pp. 2729-2737

doi:10.1109/CVPRW.2019.00331 /cvprw/2019/mishra2019cvprw-interpreting/

Abstract

This 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.

CVPRW Semantic Scholar

Cite

Text

Mishra et al. "Interpreting Fine-Grained Dermatological Classification by Deep Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00331

Markdown

[Mishra et al. "Interpreting Fine-Grained Dermatological Classification by Deep Learning." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/mishra2019cvprw-interpreting/) doi:10.1109/CVPRW.2019.00331

BibTeX

@inproceedings{mishra2019cvprw-interpreting,
  title     = {{Interpreting Fine-Grained Dermatological Classification by Deep Learning}},
  author    = {Mishra, Sourav and Imaizumi, Hideaki and Yamasaki, Toshihiko},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {2729-2737},
  doi       = {10.1109/CVPRW.2019.00331},
  url       = {https://mlanthology.org/cvprw/2019/mishra2019cvprw-interpreting/}
}