AcneGrader: An ensemble pruning of the deep learning base models to grade acne.

Journal: Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
PMID: 35639819

Abstract

BACKGROUND: Acne is one of the most common skin lesions in adolescents. Some severe or inflammatory acne leads to scars, which may have major impacts on patients' quality of life or even job prospects. Grading acne plays an important role in diagnosis, and the diagnosis is made by counting the number of acne. It is a labor-intensive job and it is easy for dermatologists to make mistakes, so it is very important to develop automatic diagnosis methods. Ensemble learning may improve the prediction results of the base models, but its time complexity is relatively high. The ensemble pruning strategy may solve this computational challenge by removing the redundant base models.

Authors

  • Shuai Liu
    Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China.
  • Yusi Fan
    College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
  • Meiyu Duan
    BioKnow Health Informatics Lab, College of Computer Science and Technology.
  • Yueying Wang
    Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, China.
  • Guoxiong Su
    Beijing Dr. of Acne Medical Research Institute, Beijing, China.
  • Yanjiao Ren
    College of Information Technology, Jilin Agricultural University, Changchun, 130118, Jilin, China.
  • Lan Huang
  • Fengfeng Zhou

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