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
N1 - Publisher Copyright:
© 2019 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85084436135&partnerID=8YFLogxK
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 -
