TY - GEN
T1 - Increasing Rosacea Awareness Among Population Using Deep Learning and Statistical Approaches
AU - Yang, Chengyu
AU - Liu, Chengjun
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Approximately 16 million Americans suffer from rosacea according to the National Rosacea Society. To increase rosacea awareness, automatic rosacea detection methods using deep learning and explainable statistical approaches are presented in this paper. The deep learning method applies the ResNet-18 for rosacea detection, and the statistical approaches utilize the means of the two classes, namely, the rosacea class vs. the normal class, and the principal component analysis to extract features from the facial images for automatic rosacea detection. The contributions of the proposed methods are three-fold. First, the proposed methods are able to automatically distinguish patients who are suffering from rosacea from people who are clean of this disease. Second, the statistical approaches address the explainability issue that allows doctors and patients to understand and trust the results. And finally, the proposed methods will not only help increase rosacea awareness in the general population but also help remind the patients who suffer from this disease of possible early treatment since rosacea is more treatable at its early stages. The code and data are available at https://github.com/chengyuyang-njit/rosacea_detection.git.
AB - Approximately 16 million Americans suffer from rosacea according to the National Rosacea Society. To increase rosacea awareness, automatic rosacea detection methods using deep learning and explainable statistical approaches are presented in this paper. The deep learning method applies the ResNet-18 for rosacea detection, and the statistical approaches utilize the means of the two classes, namely, the rosacea class vs. the normal class, and the principal component analysis to extract features from the facial images for automatic rosacea detection. The contributions of the proposed methods are three-fold. First, the proposed methods are able to automatically distinguish patients who are suffering from rosacea from people who are clean of this disease. Second, the statistical approaches address the explainability issue that allows doctors and patients to understand and trust the results. And finally, the proposed methods will not only help increase rosacea awareness in the general population but also help remind the patients who suffer from this disease of possible early treatment since rosacea is more treatable at its early stages. The code and data are available at https://github.com/chengyuyang-njit/rosacea_detection.git.
KW - Computer-Aided Diagnosis
KW - Deep Learning
KW - Explainability
KW - Principal Component Analysis
KW - Rosacea
KW - Statistical Approaches
UR - http://www.scopus.com/inward/record.url?scp=105003208999&partnerID=8YFLogxK
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U2 - 10.1007/978-981-96-3863-5_11
DO - 10.1007/978-981-96-3863-5_11
M3 - Conference contribution
AN - SCOPUS:105003208999
SN - 9789819638628
T3 - Lecture Notes in Electrical Engineering
SP - 110
EP - 119
BT - Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2024 - Medical Imaging and Computer-Aided Diagnosis
A2 - Su, Ruidan
A2 - Frangi, Alejandro F.
A2 - Zhang, Yudong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024
Y2 - 19 November 2024 through 21 November 2024
ER -