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: pdf
Pre-published on: January 12, 2021
Published on:
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 openaccess 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
in real-time.
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.