Automated Diagnosis of Acne and Rosacea using Convolution Neural Networks

Firas Gerges, Frank Shih, Danielle Azar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Acne and Rosacea are two common skin diseases that affect many people worldwide. These two skin conditions can result in similar signs, which leads to the misdiagnosis of the case. People affected by these two skin rashes, usually tend not to seek medical diagnosis from expert dermatologists, but instead rely on over-the-counter medications and beauty products for self-treatment. Although acne and rosacea are both usually considered non-dangerous, treating acne with rosacea medication (and vice-versa) can lead to worsen symptoms. In this paper, we propose a deep learning model that can automatically distinguish Rosacea from Acne cases using infected skin images. Due to the limited number of available images, we enlarged the data set using image augmentation. Experimental results show that our model achieves a high performance with an average testing accuracy of 87.1% (over 10-folds) and 91.2% on the validation set. The good predictive performance of the model depicts its usability to classify new, unseen cases. We believe that such a model can serve as an efficient basis to build an automatic acne-rosacea distinguishing software tool.

Original languageEnglish (US)
Title of host publicationAIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
PublisherAssociation for Computing Machinery
Pages607-613
Number of pages7
ISBN (Electronic)9781450384087
DOIs
StatePublished - Sep 24 2021
Externally publishedYes
Event4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021 - Virtual, Online, China
Duration: Sep 17 2021Sep 19 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021
Country/TerritoryChina
CityVirtual, Online
Period9/17/219/19/21

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Keywords

  • Acne
  • Convolutional Neural Networks
  • Image Processing
  • Machine Learning
  • Rosacea

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