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

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...

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Veröffentlicht in:Computers in biology and medicine 2021-09, Vol.136, p.104610-104610, Article 104610
Hauptverfasser: Phan, Duc Tri, Ta, Quoc Bao, Huynh, Thanh Canh, Vo, Tan Hung, Nguyen, Cong Hoan, Park, Sumin, Choi, Jaeyeop, Oh, Junghwan
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Sprache:eng
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Zusammenfassung: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. •Introducing a smart LED therapy system with a novel method for automatic facial acne vulgaris diagnosis.•An LED therapy device with different power densities and LED grid control is designed.•A deep learning model based on YOLOv2 is developed for an automatic acne diagnosis•A smartphone application is developed for facial photography image capture and LED therapy parameter configuration.•A healthcare Internet of Things (H-IoT) platform for the connectivity between smartphone apps, the cloud server, and the LED therapy device is introduced.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2021.104610