Abstract
Nowadays, facial acne is a popular skin disease. Acne is distributed in different regions on the face, and the severity of acne varies from patient to patient. Therefore, it is necessary to have an exact and objective diagnosis for each patient’s case before treatment. The problem of assessing severity of acne on human face is highly applicable in practice, as acne severity is essential for dermatologists to make a precise and standardized treatment decision. We perform surveys of automatic acne detection and classification systems. Our work follows the implementation by Xiaoping Wu et al. that grades and counts acne via label distribution learning applying on ACNE04 dataset, and the method of transfer learning regression model using image rolling data augmentation from Microsoft and Nestlé collaboration. We give discussion and conclusion about the two approaches from different experiments’ result.
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Acknowledgment
This research is funded by Advanced Program in Computer Science, the Faculty of Information Technology, University of Science, VNU-HCM, Vietnam.
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Nguyen, A., Thai, H., Le, T. (2021). Severity Assessment of Facial Acne. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_45
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DOI: https://doi.org/10.1007/978-3-030-88081-1_45
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