PoS - Proceedings of Science
Volume 372 - Artificial Intelligence for Science, Industry and Society (AISIS2019) - Day 2
Skin Lesion Detection in Dermatological Images using Deep Learning
J.C. Moreno-Tagle,* J. Olveres, B. Escalante-Ramírez
*corresponding author
Full text: Not available
This paper demonstrates that it is possible to approach the skin lesion classification problem as a detection problem, a much more complex and interesting problem, by training a deep neural network based detection architecture and applying image processing techniques to a dermatology dataset as part of the data augmentation strategy with satisfactory and promising results. The image dataset used in the experiments comes from the ISIC Dermoscopic Archive, an open-access dermatology repository. In particular, the ISIC 2017 dataset, a subset of the ISIC archive, released for the annual ISIC challenge was used. We show that it is possible to adapt a high quality imaging dataset to the requirements demanded by a deep learning detection architecture such as YOLOv3. In conjunction with image processing techniques as a previous step, the deep neural network was successfully trained to identify and locate three different types of skin lesions.
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.