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Acne detection and severity evaluation with interpretable convolutional neural network models.

Hao Wen^1,2, Wenjian Yu¹, Yuanqing Wu²

¹Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|February 6, 2022

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Deep learning models accurately locate acne lesions on facial images, enabling reliable acne severity assessment. A freely accessible app provides continuous self-monitoring for patients and aids dermatologists in treatment evaluation.

Area of Science:

Dermatology
Computer Vision
Artificial Intelligence

Background:

Acne vulgaris is a common skin condition impacting physical and mental well-being.
Early diagnosis and self-monitoring are crucial for managing acne discomfort.

Purpose of the Study:

Develop and compare deep learning models for acne lesion detection on facial images.
Estimate facial acne severity using established medical criteria.

Main Methods:

Utilized object detection models with convolutional neural networks (CNNs) as backbones.
Focused on models offering enhanced interpretability for credible acne analysis.

Main Results:

Validated model effectiveness using real-world facial image data.

Keywords:

Facial acne convolutional neural network interpretability object detection self-monitoring

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Published on: April 21, 2023

Achieved a maximum mean average precision (mAP) of 0.536 on an open-source dataset.

Demonstrated low error rates in acne lesion counting (0.43 ± 6.65).

Conclusions:

Developed models are publicly available via a free WeChat applet service.
The app enables continuous out-of-hospital self-monitoring for acne patients.
Facilitates dermatologists' tracking of disease progression and treatment efficacy.