TY - GEN
T1 - An Intelligent Scalp Inspection and Diagnosis System for Caring Hairy Scalp Health
AU - Su, Jian Ping
AU - Chen, Liang Bi
AU - Hsu, Chia Hao
AU - Wang, Wei Chien
AU - Kuo, Cheng Chin
AU - Chang, Wan Jung
AU - Hu, Wei Wen
AU - Lee, Da Huei
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/12
Y1 - 2018/12/12
N2 - This paper proposes an intelligent scalp inspection and diagnosis system based on the deep learning techniques for caring hair scalp health. The proposed system can automatically recognize the status of the user's scalp. Moreover, we can continuously increase in the number of samples to enhance the accuracy rate by adopting deep learning techniques. The proposed system consists of a scalp detector, an app running on a tablet, and a cloud management platform. The scalp detector will be connected with the tablet via Wi-Fi wireless network. Thus, a scalp photo can be captured via the proposed scalp detector. The scalp photo will be recognized by scalp detector, and the recognized result of the scalp will also be sent and displayed to the tablet. As a result, we can get the quantitative data on the scalp, including bacteria, allergies, dandruff, grease, and hair loss. Moreover. The experimental results showed that the accuracy can be achieved 90.909%.
AB - This paper proposes an intelligent scalp inspection and diagnosis system based on the deep learning techniques for caring hair scalp health. The proposed system can automatically recognize the status of the user's scalp. Moreover, we can continuously increase in the number of samples to enhance the accuracy rate by adopting deep learning techniques. The proposed system consists of a scalp detector, an app running on a tablet, and a cloud management platform. The scalp detector will be connected with the tablet via Wi-Fi wireless network. Thus, a scalp photo can be captured via the proposed scalp detector. The scalp photo will be recognized by scalp detector, and the recognized result of the scalp will also be sent and displayed to the tablet. As a result, we can get the quantitative data on the scalp, including bacteria, allergies, dandruff, grease, and hair loss. Moreover. The experimental results showed that the accuracy can be achieved 90.909%.
UR - http://www.scopus.com/inward/record.url?scp=85060288717&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060288717&partnerID=8YFLogxK
U2 - 10.1109/GCCE.2018.8574619
DO - 10.1109/GCCE.2018.8574619
M3 - Conference contribution
AN - SCOPUS:85060288717
T3 - 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018
SP - 464
EP - 465
BT - 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Global Conference on Consumer Electronics, GCCE 2018
Y2 - 9 October 2018 through 12 October 2018
ER -