Interactive skin condition recognition

Orod Razeghi; Qian Zhang; Guoping Qiu

doi:10.1109/ICME.2013.6607635

Interactive skin condition recognition

Orod Razeghi
, Qian Zhang
, Guoping Qiu

School of Computer Science

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

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 language	English
Title of host publication	2013 IEEE International Conference on Multimedia and Expo, ICME 2013
DOIs	https://doi.org/10.1109/ICME.2013.6607635
Publication status	Published - 2013
Event	2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, United States Duration: 15 Jul 2013 → 19 Jul 2013

Publication series

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

Conference

Conference	2013 IEEE International Conference on Multimedia and Expo, ICME 2013
Country/Territory	United States
City	San Jose, CA
Period	15/07/13 → 19/07/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Free Keywords

dermatology
human in the loop
skin conditions
visual recognition

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications

Access to Document

10.1109/ICME.2013.6607635

Cite this

@inproceedings{75690df07c6848c1af190ce00dd768c5,

title = "Interactive skin condition recognition",

abstract = "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.",

keywords = "dermatology, human in the loop, skin conditions, visual recognition",

author = "Orod Razeghi and Qian Zhang and Guoping Qiu",

year = "2013",

doi = "10.1109/ICME.2013.6607635",

language = "English",

isbn = "9781479900152",

series = "Proceedings - IEEE International Conference on Multimedia and Expo",

booktitle = "2013 IEEE International Conference on Multimedia and Expo, ICME 2013",

note = "2013 IEEE International Conference on Multimedia and Expo, ICME 2013 ; Conference date: 15-07-2013 Through 19-07-2013",

}

Razeghi, O, Zhang, Q & Qiu, G 2013, Interactive skin condition recognition. in 2013 IEEE International Conference on Multimedia and Expo, ICME 2013., 6607635, Proceedings - IEEE International Conference on Multimedia and Expo, 2013 IEEE International Conference on Multimedia and Expo, ICME 2013, San Jose, CA, United States, 15/07/13. https://doi.org/10.1109/ICME.2013.6607635

TY - GEN

T1 - Interactive skin condition recognition

AU - Razeghi, Orod

AU - Zhang, Qian

AU - Qiu, Guoping

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - dermatology

KW - human in the loop

KW - skin conditions

KW - visual recognition

UR - https://www.scopus.com/pages/publications/84885624212

U2 - 10.1109/ICME.2013.6607635

DO - 10.1109/ICME.2013.6607635

M3 - Conference contribution

AN - SCOPUS:84885624212

SN - 9781479900152

T3 - Proceedings - IEEE International Conference on Multimedia and Expo

BT - 2013 IEEE International Conference on Multimedia and Expo, ICME 2013

T2 - 2013 IEEE International Conference on Multimedia and Expo, ICME 2013

Y2 - 15 July 2013 through 19 July 2013

ER -

Interactive skin condition recognition

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

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