TY - GEN
T1 - A Decision Machine Learning Support System for Human Skin Disease Classifier
AU - Banditsingha, Pakkapat
AU - Thaipisutikul, Tipajin
AU - Shih, Timothy K.
AU - Lin, Chih Yang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - For the past decades, the prevalence of dermatological disorders, especially human skin diseases, has been rising. The majority of these diseases are contagious and are also based on visual perceptions. Although many works have shown promising results on the image classification problem, only a few studies compare traditional machine learning models and the recent deep learning models with various metrics on human skin diseases classification. Therefore, in this paper, we propose A Decision Machine Learning Support System for Human Skin Disease Classifier (DSSC) to classify five skin disease classes, including 750 images gained from the Dermnet dataset. In particular, we perform image pre-processing, image resizing, image interpolation, and image augmentation to adjust the input images into the proper format for all models. Through the extensive experiments, RestNet50 outperforms other deep learning and traditional machine learning methods in all metrics, including accuracy, precision, recall, and F-measure by a large margin.
AB - For the past decades, the prevalence of dermatological disorders, especially human skin diseases, has been rising. The majority of these diseases are contagious and are also based on visual perceptions. Although many works have shown promising results on the image classification problem, only a few studies compare traditional machine learning models and the recent deep learning models with various metrics on human skin diseases classification. Therefore, in this paper, we propose A Decision Machine Learning Support System for Human Skin Disease Classifier (DSSC) to classify five skin disease classes, including 750 images gained from the Dermnet dataset. In particular, we perform image pre-processing, image resizing, image interpolation, and image augmentation to adjust the input images into the proper format for all models. Through the extensive experiments, RestNet50 outperforms other deep learning and traditional machine learning methods in all metrics, including accuracy, precision, recall, and F-measure by a large margin.
KW - decision support system
KW - deep learning
KW - image processing
KW - machine learning
KW - skin disease classification
UR - http://www.scopus.com/inward/record.url?scp=85127540137&partnerID=8YFLogxK
U2 - 10.1109/ECTIDAMTNCON53731.2022.9720379
DO - 10.1109/ECTIDAMTNCON53731.2022.9720379
M3 - 會議論文篇章
AN - SCOPUS:85127540137
T3 - 7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022
SP - 200
EP - 204
BT - 7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022
Y2 - 26 January 2022 through 28 January 2022
ER -