Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System

I. Ketut Eddy Purnama; Arta Kusuma Hernanda; Anak Agung Putri Ratna; Ingrid Nurtanio; Afif Nurul Hidayati; Mauridhi Hery Purnomo; Supeno Mardi Susiki Nugroho; Reza Fuad Rachmadi

doi:10.1109/CENIM48368.2019.8973303

Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System

I. Ketut Eddy Purnama
, Arta Kusuma Hernanda
, Anak Agung Putri Ratna
, Ingrid Nurtanio
, Afif Nurul Hidayati
, Mauridhi Hery Purnomo
, Supeno Mardi Susiki Nugroho
, Reza Fuad Rachmadi

Department of Computer Engineering

University of Indonesia
Universitas Hasanuddin
Universitas Airlangga

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

23 Citations (Scopus)

Abstract

We have proposed a system of classification and detection of skin diseases that can be applied to Teledermatology. This system will classify skin diseases on dermoscopic images using the Deep Learning algorithm, Convolutional Neural Network (CNN). Dermoscopic image data in this study from MNIST HAM10000 dataset which amounts to 10,015 images and published by International Skin Image Collaboration (ISIC). The dataset is divided into seven class of skin diseases which fall into the category of skin cancer. The image classification process will use two pre-trained CNN models, MobileNet v1 and Inception V3. The model results from the learning process will be applied to a web-classifier. The comparison of predictive accuracy shows that the web-classifier using the CNN Inception V3 model has an accuracy value of 72% while the web-classifier that uses the MobileNet v1 model has an accuracy value of 58%.

Original language	English
Title of host publication	2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728129655
DOIs	https://doi.org/10.1109/CENIM48368.2019.8973303
Publication status	Published - Nov 2019
Event	2nd International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Surabaya, Indonesia Duration: 19 Nov 2019 → 20 Nov 2019

Publication series

Name	2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding
Volume	2019-November

Conference

Conference	2nd International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019
Country/Territory	Indonesia
City	Surabaya
Period	19/11/19 → 20/11/19

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Convolutional Neural Network
Deep Learning
Dermoscopic Image
Skin Diseases

Access to Document

10.1109/CENIM48368.2019.8973303

Cite this

Purnama, I. K. E., Hernanda, A. K., Ratna, A. A. P., Nurtanio, I., Hidayati, A. N., Purnomo, M. H., Nugroho, S. M. S., & Rachmadi, R. F. (2019). Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System. In 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding Article 8973303 (2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding; Vol. 2019-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CENIM48368.2019.8973303

Purnama, I. Ketut Eddy ; Hernanda, Arta Kusuma ; Ratna, Anak Agung Putri et al. / Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System. 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding).

@inproceedings{749d826fc11d4cd6ac160407308f9b8c,

title = "Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System",

abstract = "We have proposed a system of classification and detection of skin diseases that can be applied to Teledermatology. This system will classify skin diseases on dermoscopic images using the Deep Learning algorithm, Convolutional Neural Network (CNN). Dermoscopic image data in this study from MNIST HAM10000 dataset which amounts to 10,015 images and published by International Skin Image Collaboration (ISIC). The dataset is divided into seven class of skin diseases which fall into the category of skin cancer. The image classification process will use two pre-trained CNN models, MobileNet v1 and Inception V3. The model results from the learning process will be applied to a web-classifier. The comparison of predictive accuracy shows that the web-classifier using the CNN Inception V3 model has an accuracy value of 72\% while the web-classifier that uses the MobileNet v1 model has an accuracy value of 58\%.",

keywords = "Convolutional Neural Network, Deep Learning, Dermoscopic Image, Skin Diseases",

author = "Purnama, \{I. Ketut Eddy\} and Hernanda, \{Arta Kusuma\} and Ratna, \{Anak Agung Putri\} and Ingrid Nurtanio and Hidayati, \{Afif Nurul\} and Purnomo, \{Mauridhi Hery\} and Nugroho, \{Supeno Mardi Susiki\} and Rachmadi, \{Reza Fuad\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2nd International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 ; Conference date: 19-11-2019 Through 20-11-2019",

year = "2019",

month = nov,

doi = "10.1109/CENIM48368.2019.8973303",

language = "English",

series = "2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding",

address = "United States",

}

Purnama, IKE , Hernanda, AK, Ratna, AAP, Nurtanio, I, Hidayati, AN, Purnomo, MH , Nugroho, SMS & Rachmadi, RF 2019, Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System. in 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding., 8973303, 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding, vol. 2019-November, Institute of Electrical and Electronics Engineers Inc., 2nd International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019, Surabaya, Indonesia, 19/11/19. https://doi.org/10.1109/CENIM48368.2019.8973303

Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System. / Purnama, I. Ketut Eddy ; Hernanda, Arta Kusuma; Ratna, Anak Agung Putri et al.
2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2019. 8973303 (2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding; Vol. 2019-November).

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

TY - GEN

T1 - Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System

AU - Purnama, I. Ketut Eddy

AU - Hernanda, Arta Kusuma

AU - Ratna, Anak Agung Putri

AU - Nurtanio, Ingrid

AU - Hidayati, Afif Nurul

AU - Purnomo, Mauridhi Hery

AU - Nugroho, Supeno Mardi Susiki

AU - Rachmadi, Reza Fuad

PY - 2019/11

Y1 - 2019/11

N2 - We have proposed a system of classification and detection of skin diseases that can be applied to Teledermatology. This system will classify skin diseases on dermoscopic images using the Deep Learning algorithm, Convolutional Neural Network (CNN). Dermoscopic image data in this study from MNIST HAM10000 dataset which amounts to 10,015 images and published by International Skin Image Collaboration (ISIC). The dataset is divided into seven class of skin diseases which fall into the category of skin cancer. The image classification process will use two pre-trained CNN models, MobileNet v1 and Inception V3. The model results from the learning process will be applied to a web-classifier. The comparison of predictive accuracy shows that the web-classifier using the CNN Inception V3 model has an accuracy value of 72% while the web-classifier that uses the MobileNet v1 model has an accuracy value of 58%.

AB - We have proposed a system of classification and detection of skin diseases that can be applied to Teledermatology. This system will classify skin diseases on dermoscopic images using the Deep Learning algorithm, Convolutional Neural Network (CNN). Dermoscopic image data in this study from MNIST HAM10000 dataset which amounts to 10,015 images and published by International Skin Image Collaboration (ISIC). The dataset is divided into seven class of skin diseases which fall into the category of skin cancer. The image classification process will use two pre-trained CNN models, MobileNet v1 and Inception V3. The model results from the learning process will be applied to a web-classifier. The comparison of predictive accuracy shows that the web-classifier using the CNN Inception V3 model has an accuracy value of 72% while the web-classifier that uses the MobileNet v1 model has an accuracy value of 58%.

KW - Convolutional Neural Network

KW - Deep Learning

KW - Dermoscopic Image

KW - Skin Diseases

UR - https://www.scopus.com/pages/publications/85084436135

U2 - 10.1109/CENIM48368.2019.8973303

DO - 10.1109/CENIM48368.2019.8973303

M3 - Conference contribution

AN - SCOPUS:85084436135

T3 - 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding

BT - 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019

Y2 - 19 November 2019 through 20 November 2019

ER -

Purnama IKE , Hernanda AK, Ratna AAP, Nurtanio I, Hidayati AN, Purnomo MH et al. Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System. In 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding. Institute of Electrical and Electronics Engineers Inc. 2019. 8973303. (2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding). doi: 10.1109/CENIM48368.2019.8973303

Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this