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Acne Scan - Application In Assessment Of Acne And Supports Treatment Road

9 pagesPublished: January 16, 2022

Abstract

As society develops, many aspects of life are concerned by people, including facial skincare, avoiding acne-related diseases. In this work, we will propose a complete solution for treating acne at home, including 4 processors. First, the anomaly detector uses image processing techniques by Multi-Threshold and Color Segmentation, depending on each color channel corresponding to each type of acne. The sensitivity of the detector is 89.4%. Second, the set of anomalies classifiers into 6 main categories, including 4 major acne types and 2 non-acne types. By applying the convolutional neural model, the accuracy, sensitivity, and F1 are 84.17%, 81.5%, and 82%, respectively. Third, the acne status assessment kit is based on the mGAGS method to classify the condition of a face as mild, moderate, severe, or very severe with an accuracy of 81.25%. Finally, the product recommender, which generalizes from the results of the previous processors with an accuracy of 70-90%. This is the premise that helps doctors as well as general users to evaluate the level of acne on a face effectively and save time.

Keyphrases: acne classification, Acne detection, Acne status assessment, Suggestion System

In: Tich Thien Truong, Trung Nghia Tran, Thanh Nha Nguyen and Quoc Khai Le (editors). Proceedings of International Symposium on Applied Science 2021, vol 4, pages 122--130

Links:
BibTeX entry
@inproceedings{ISAS2021:Acne_Scan__Application,
  author    = {Nguyen Xuan Nguyen Pham and Thi Tham Tran and Minh Thang Do and Ngoc Bao Duy Tran},
  title     = {Acne Scan - Application In Assessment Of Acne And Supports Treatment Road},
  booktitle = {Proceedings of International Symposium on Applied Science 2021},
  editor    = {Tich Thien Truong and Trung Nghia Tran and Thanh Nha Nguyen and Quoc Khai Le},
  series    = {Kalpa Publications in Engineering},
  volume    = {4},
  pages     = {122--130},
  year      = {2022},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1770},
  url       = {https://easychair.org/publications/paper/3rVC},
  doi       = {10.29007/bg75}}
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