Smart Low Level Laser Therapy System for Automatic Facial Dermatological Disorder Diagnosis

Computer-aided diagnosis using dermoscopy images is a promising technique for improving the efficiency of facial skin disorder diagnosis and treatment. Hence, in this study, we propose a low-level laser therapy (LLLT) system with a deep neural network and medical internet of things (MIoT) assistance. The main contributions of this study are to (1) provide a comprehensive hardware and software design for an automatic phototherapy system, (2) propose a modified-U(2)Net deep learning model for facial dermatological disorder segmentation, and (3) develop a synthetic data generation process for the proposed models to address the issue of the limited and imbalanced dataset. Finally, a MIoT-assisted LLLT platform for remote healthcare monitoring and management is proposed. The trained U-2-Net model achieved a better performance on untrained dataset than other recent models, with an average Accuracy of 97.5%, Jaccard index of 74.7%, and Dice coefficient of 80.6%. The experimental results demonstrated that our proposed LLLT system can accurately segment facial skin diseases and automatically apply for phototherapy. The integration of artificial intelligence and MIoT-based healthcare platforms is a significant step toward the development of medical assistant tools in the near future.

Automated summary

In this study, a low-level laser therapy (LLLT) system with a deep neural network and medical internet of things (MIoT) assistance is proposed for computer-aided diagnosis using dermoscopy images. The main contributions of this study include a comprehensive hardware and software design for an automatic phototherapy system, a modified-U(2)Net deep learning model for facial dermatological disorder segmentation, and the development of a synthetic data generation process to address the issue of the limited and imbalanced dataset.