Advancements in acne detection: application of the CenterNet network in smart dermatology

作者全名："Zhang, Daojun; Li, Huanyu; Shi, Jiajia; Shen, Yue; Zhu, Ling; Chen, Nianze; Wei, Zikun; Lv, Junwei; Chen, Yu; Hao, Fei"

作者地址："[Zhang, Daojun; Hao, Fei] Chongqing Med Univ, Affiliated Hosp 3, Chongqing, Peoples R China; [Li, Huanyu; Shi, Jiajia; Zhu, Ling; Chen, Nianze; Wei, Zikun; Lv, Junwei] Shanghai Beforteen AI Lab, Shanghai, Peoples R China; [Shen, Yue; Chen, Yu] Univ Tokyo, Grad Sch Frontier Sci, Dept Human & Engn Environm Studies, Simulat Complex Syst Lab, Tokyo, Japan"

通信作者："Hao, F (通讯作者)，Chongqing Med Univ, Affiliated Hosp 3, Chongqing, Peoples R China."

来源：FRONTIERS IN MEDICINE

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001197825800001

JCR分区：Q1

影响因子：3.1

年份：2024

卷号：11

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：CenterNet network; acne detection; dermatology; deep learning in healthcare; image detection; interpretability

摘要："Introduction Acne detection is critical in dermatology, focusing on quality control of acne imagery, precise segmentation, and grading. Traditional research has been limited, typically concentrating on singular aspects of acne detection.Methods We propose a multi-task acne detection method, employing a CenterNet-based training paradigm to develop an advanced detection system. This system collects acne images via smartphones and features multi-task capabilities for detecting image quality and identifying various acne types. It differentiates between noninflammatory acne, papules, pustules, nodules, and provides detailed delineation for cysts and post-acne scars.Results The implementation of this multi-task learning-based framework in clinical diagnostics demonstrated an 83% accuracy in lesion categorization, surpassing ResNet18 models by 12%. Furthermore, it achieved a 76% precision in lesion stratification, outperforming dermatologists by 16%.Discussion Our framework represents a advancement in acne detection, offering a comprehensive tool for classification, localization, counting, and precise segmentation. It not only enhances the accuracy of remote acne lesion identification by doctors but also clarifies grading logic and criteria, facilitating easier grading judgments."

基金机构：Chongqing Talent Program "Package Project" [cstc2021ycjh-bgzxm0291]

基金资助正文："Thanks for public dataset ""MedDialog: Large-scale Medical Dialogue Datasets."" We appreciate the effort of the Third Affiliated Hospital of Chongqing Medical University (CQMU), the Army Medical Center, and Chongqing Shapingba District People's Hospital.r The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Chongqing Talent Program ""Package Project"" [grant number: cstc2021ycjh-bgzxm0291 (DZ)]."