A smart LED therapy device with an automatic facial acne vulgaris diagnosis based on deep learning and internet of things application.

Journal: Computers in biology and medicine
PMID: 34274598

Abstract

In low-level laser therapy, providing an optimal dosage and proposing a proper diagnosis before dermatological treatment are essential to reduce the side effects and potential dangers. In this article, a smart LED therapy system for automatic facial acne vulgaris diagnosis based on deep learning and Internet of Things application is proposed. The main goals of this study were to (1) develop an LED therapy device with different power densities and LED grid control; (2) propose a deep learning model based on modified ResNet50 and YOLOv2 for an automatic acne diagnosis; and (3) develop a smartphone application for facial photography image capture and LED therapy parameter configuration. Furthermore, a healthcare Internet of Things (H-IoT) platform for the connectivity between smartphone apps, the cloud server, and the LED therapy device is proposed to improve the efficiency of the treatment process. Experiments were conducted on test data sets divided by a cross-validation method to verify the feasibility of the proposed LED therapy system with automatic facial acne detection. The obtained results evidenced the practical application of the proposed LED therapy system for automatic acne diagnosis and H-IoT-based solutions.

Authors

  • Duc Tri Phan
    Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.
  • Quoc Bao Ta
    Department of Ocean Engineering, Pukyong National University, Nam-gu, Busan, 48513, South Korea.
  • Thanh Canh Huynh
    Center for Construction, Mechanics and Materials, Institute of Research and Development, Duy Tan University, 03 Quang Trung, Hai Chau, Danang, 550000, Viet Nam; Faculty of Civil Engineering, Duy Tan University, 03 Quang Trung, Hai Chau, Danang, 550000, Viet Nam.
  • Tan Hung Vo
    Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.
  • Cong Hoan Nguyen
    Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan, 48513, South Korea; BK21 FOUR 'New-senior' Oriented Smart Health Care Education, Pukyong National University, Busan, 48513, South Korea.
  • Sumin Park
    School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
  • Jaeyeop Choi
    Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea.
  • Junghwan Oh
    Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Republic of Korea; Biomedical Engineering, Pukyong National University, Republic of Korea; Ohlabs Corporation, Busan 48513, Korea.. Electronic address: jungoh@pknu.ac.kr.

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