Interactive skin condition recognition

Orod Razeghi, Qian Zhang, Guoping Qiu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)


It is believed that there are between 1000 to 2000 skin conditions, and about 20% are difficult to diagnose. An intelligent system capable of making accurate diagnosis not only helps patients in places where access to health services are scarce, but also benefits typical general practitioners who have received minimal dermatology training. In this paper, we introduce a challenging dataset developed by gathering 2309 images from 44 different skin conditions, and collecting answers to simple perceptual questions from 361 'Amazon Mechanical Turk' workers. We also propose a method based on random forest technology that combines visual features of the skin lesion images with user provided answers to achieve promising recognition rates. We believe that our solution can be potentially improved and installed on smart phones and tablets to enhance quality of life in patients across the world.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Multimedia and Expo, ICME 2013
Publication statusPublished - 2013
Event2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, United States
Duration: 15 Jul 201319 Jul 2013

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


Conference2013 IEEE International Conference on Multimedia and Expo, ICME 2013
Country/TerritoryUnited States
CitySan Jose, CA


  • dermatology
  • human in the loop
  • skin conditions
  • visual recognition

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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