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An effective vitiligo intelligent classification system

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Abstract

Vitiligo is one of the most common skin diseases in the world. According to the World Health Organization (WHO), the number of people suffering from vitiligo is growing year by year and vitiligo becomes a worldwide problem. In order to helping doctors with vitiligo diagnosis, we propose a vitiligo artificial intelligence diagnosis system. It is able to generate vitiligo images in Wood Lamp with high resolution and classify these images with high precision. In our system, we employ Cycle-Consistent Adversarial Networks (Cycle GAN) to generate images in Wood Lamp. What’s more, we use an advanced super resolution method, Attention-Aware DenseNet with Residual Deconvolution (ADRD), to improve the resolution of images. Finally, we obtain fantastic classification results with Resnet50. Our system is found to achieve the classification performance of 85.69% accuracy, which is increased by 9.32% compared with using Resnet50 to classify original images directly. The optimization and expansion of the system depend on the increase of data set and the improvement of system’s modules.

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Funding

This work is supported by 2018 Sugon Intelligent-Factory on Advanced Computing Devices (No. MIIT2018-265-137), Fundamental Research Funds for the Central Universities (No. FRF-DF-19-011).

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Author notes

  1. Wei Luo and Jian Liu are co-first authors.

Authors and Affiliations

  1. Department of Dermatology, Air Force Special Medical Center, PLA, Beijing, 100142, China

    Wei Luo

  2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Jian Liu & Yidi Huang

  3. Medical Research Department, Air Force Special Medical Center, PLA, Beijing, 100142, China

    Ning Zhao

Authors
  1. Wei Luo
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  2. Jian Liu
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  3. Yidi Huang
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  4. Ning Zhao
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by WL and NZ. The system framework is designed by JL. The first draft of the manuscript was written by YH and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jian Liu.

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Luo, W., Liu, J., Huang, Y. et al. An effective vitiligo intelligent classification system. J Ambient Intell Human Comput 14, 5479–5488 (2023). https://doi.org/10.1007/s12652-020-02357-5

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  • DOI: https://doi.org/10.1007/s12652-020-02357-5

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