Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

Xin He; Shihao Wang; Shaohuai Shi; Zhenheng Tang; Yuxin Wang; Zhihao Zhao; Jing Dai; Ronghao Ni; Xiaofeng Zhang; Xiaoming Liu; Zhili Wu; Wu Yu; Xiaowen Chu

doi:10.1109/BigData47090.2019.9006528

Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

Xin He
, Shihao Wang
, Shaohuai Shi
, Zhenheng Tang
, Yuxin Wang
, Zhihao Zhao
, Jing Dai
, Ronghao Ni
, Xiaofeng Zhang
, Xiaoming Liu
, Zhili Wu
, Wu Yu
, Xiaowen Chu^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

19 Citations (Scopus)

Abstract

Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of experienced dermatologists in many countries. To alleviate this problem, computer-aided diagnosis with state-of-the-art (SOTA) machine learning techniques would be a promising solution. In this paper, we aim at understanding the performance of convolutional neural network (CNN) based approaches. We first build two versions of skin disease datasets from Internet images: (a) Skin-10, which contains 10 common classes of skin disease with a total of 10,218 images; (b) Skin-100, which is a larger dataset that consists of 19,807 images of 100 skin disease classes. Based on these datasets, we benchmark several SOTA CNN models and show that the accuracy of skin-100 is much lower than the accuracy of skin-10. We then implement an ensemble method based on several CNN models and achieve the best accuracy of 79.01% for Skin-10 and 53.54% for Skin-100. We also present an object detection based approach by introducing bounding boxes into the Skin-10 dataset. Our results show that object detection can help improve the accuracy of some skin disease classes.

Original language	English
Title of host publication	2019 IEEE International Conference on Big Data (Big Data)
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	IEEE
Pages	4839-4844
Number of pages	6
ISBN (Electronic)	9781728108582, 9781728108575
ISBN (Print)	9781728108599
DOIs	https://doi.org/10.1109/BigData47090.2019.9006528
Publication status	Published - Dec 2019
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: 9 Dec 2019 → 12 Dec 2019

Publication series

Name	Proceedings - IEEE International Conference on Big Data

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	9/12/19 → 12/12/19

UN SDGs

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

SDG 3 Good Health and Well-being

User-Defined Keywords

CNN
computer-aided skin disease diagnosis
ensemble method
object detection

Access to Document

10.1109/BigData47090.2019.9006528

Cite this

He, X., Wang, S., Shi, S., Tang, Z., Wang, Y., Zhao, Z., Dai, J., Ni, R., Zhang, X., Liu, X., Wu, Z., Yu, W., & Chu, X. (2019). Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), 2019 IEEE International Conference on Big Data (Big Data) (pp. 4839-4844). (Proceedings - IEEE International Conference on Big Data). IEEE. https://doi.org/10.1109/BigData47090.2019.9006528

He, Xin ; Wang, Shihao ; Shi, Shaohuai et al. / Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models. 2019 IEEE International Conference on Big Data (Big Data). editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. IEEE, 2019. pp. 4839-4844 (Proceedings - IEEE International Conference on Big Data).

@inproceedings{7fed73180bcc472ebd98c462736b1c8b,

title = "Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models",

abstract = "Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of experienced dermatologists in many countries. To alleviate this problem, computer-aided diagnosis with state-of-the-art (SOTA) machine learning techniques would be a promising solution. In this paper, we aim at understanding the performance of convolutional neural network (CNN) based approaches. We first build two versions of skin disease datasets from Internet images: (a) Skin-10, which contains 10 common classes of skin disease with a total of 10,218 images; (b) Skin-100, which is a larger dataset that consists of 19,807 images of 100 skin disease classes. Based on these datasets, we benchmark several SOTA CNN models and show that the accuracy of skin-100 is much lower than the accuracy of skin-10. We then implement an ensemble method based on several CNN models and achieve the best accuracy of 79.01\% for Skin-10 and 53.54\% for Skin-100. We also present an object detection based approach by introducing bounding boxes into the Skin-10 dataset. Our results show that object detection can help improve the accuracy of some skin disease classes.",

keywords = "CNN, computer-aided skin disease diagnosis, ensemble method, object detection",

author = "Xin He and Shihao Wang and Shaohuai Shi and Zhenheng Tang and Yuxin Wang and Zhihao Zhao and Jing Dai and Ronghao Ni and Xiaofeng Zhang and Xiaoming Liu and Zhili Wu and Wu Yu and Xiaowen Chu",

note = "Publisher copyright: {\textcopyright} 2019 IEEE; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9006528",

language = "English",

isbn = "9781728108599",

series = "Proceedings - IEEE International Conference on Big Data",

publisher = "IEEE",

pages = "4839--4844",

editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, \{Xiaohua Tony\} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, \{Yanfang Fanny\}",

booktitle = "2019 IEEE International Conference on Big Data (Big Data)",

address = "United States",

}

He, X, Wang, S, Shi, S, Tang, Z, Wang, Y, Zhao, Z, Dai, J, Ni, R, Zhang, X, Liu, X, Wu, Z, Yu, W & Chu, X 2019, Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), 2019 IEEE International Conference on Big Data (Big Data). Proceedings - IEEE International Conference on Big Data, IEEE, pp. 4839-4844, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 9/12/19. https://doi.org/10.1109/BigData47090.2019.9006528

Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models. / He, Xin; Wang, Shihao; Shi, Shaohuai et al.
2019 IEEE International Conference on Big Data (Big Data). ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. IEEE, 2019. p. 4839-4844 (Proceedings - IEEE International Conference on Big Data).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

AU - He, Xin

AU - Wang, Shihao

AU - Shi, Shaohuai

AU - Tang, Zhenheng

AU - Wang, Yuxin

AU - Zhao, Zhihao

AU - Dai, Jing

AU - Ni, Ronghao

AU - Zhang, Xiaofeng

AU - Liu, Xiaoming

AU - Wu, Zhili

AU - Yu, Wu

AU - Chu, Xiaowen

PY - 2019/12

Y1 - 2019/12

N2 - Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of experienced dermatologists in many countries. To alleviate this problem, computer-aided diagnosis with state-of-the-art (SOTA) machine learning techniques would be a promising solution. In this paper, we aim at understanding the performance of convolutional neural network (CNN) based approaches. We first build two versions of skin disease datasets from Internet images: (a) Skin-10, which contains 10 common classes of skin disease with a total of 10,218 images; (b) Skin-100, which is a larger dataset that consists of 19,807 images of 100 skin disease classes. Based on these datasets, we benchmark several SOTA CNN models and show that the accuracy of skin-100 is much lower than the accuracy of skin-10. We then implement an ensemble method based on several CNN models and achieve the best accuracy of 79.01% for Skin-10 and 53.54% for Skin-100. We also present an object detection based approach by introducing bounding boxes into the Skin-10 dataset. Our results show that object detection can help improve the accuracy of some skin disease classes.

AB - Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of experienced dermatologists in many countries. To alleviate this problem, computer-aided diagnosis with state-of-the-art (SOTA) machine learning techniques would be a promising solution. In this paper, we aim at understanding the performance of convolutional neural network (CNN) based approaches. We first build two versions of skin disease datasets from Internet images: (a) Skin-10, which contains 10 common classes of skin disease with a total of 10,218 images; (b) Skin-100, which is a larger dataset that consists of 19,807 images of 100 skin disease classes. Based on these datasets, we benchmark several SOTA CNN models and show that the accuracy of skin-100 is much lower than the accuracy of skin-10. We then implement an ensemble method based on several CNN models and achieve the best accuracy of 79.01% for Skin-10 and 53.54% for Skin-100. We also present an object detection based approach by introducing bounding boxes into the Skin-10 dataset. Our results show that object detection can help improve the accuracy of some skin disease classes.

KW - CNN

KW - computer-aided skin disease diagnosis

KW - ensemble method

KW - object detection

UR - https://www.scopus.com/pages/publications/85081343538

U2 - 10.1109/BigData47090.2019.9006528

DO - 10.1109/BigData47090.2019.9006528

M3 - Conference proceeding

AN - SCOPUS:85081343538

SN - 9781728108599

T3 - Proceedings - IEEE International Conference on Big Data

SP - 4839

EP - 4844

BT - 2019 IEEE International Conference on Big Data (Big Data)

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - IEEE

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

He X, Wang S, Shi S, Tang Z, Wang Y, Zhao Z et al. Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, 2019 IEEE International Conference on Big Data (Big Data). IEEE. 2019. p. 4839-4844. (Proceedings - IEEE International Conference on Big Data). doi: 10.1109/BigData47090.2019.9006528

Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

Abstract

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UN SDGs

User-Defined Keywords

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