{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:39:03Z","timestamp":1777891143832,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["THC"],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:p>BACKGROUND: Acne is a skin lesion type widely existing in adolescents, and poses computational challenges for automatic diagnosis. Computer vision algorithms are utilized to detect and determine different subtypes of acne. Most of the existing acne detection algorithms are based on the facial natural images, which carry noisy factors like illuminations. OBJECTIVE: In order to tackle this issue, this study collected a dataset ACNEDer of dermoscopic acne images with annotations. Deep learning methods have demonstrated powerful capabilities in automatic acne diagnosis, and they usually release the training epoch with the best performance as the delivered model. METHODS: This study proposes a novel self-ensemble and stacking-based framework AcneTyper for diagnosing the acne subtypes. Instead of delivering the best epoch, AcneTyper consolidates the prediction results of all training epochs as the latent features and stacks the best subset of these latent features for distinguishing different acne subtypes. RESULTS: The proposed AcneTyper framework achieves a promising detection performance of acne subtypes and even outperforms a clinical dermatologist with two-year experiences by 6.8% in accuracy. CONCLUSION: The method we proposed is used to determine different subtypes of acne and outperforms inexperienced dermatologists and contributes to reducing the probability of misdiagnosis.<\/jats:p>","DOI":"10.3233\/thc-220295","type":"journal-article","created":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T14:46:54Z","timestamp":1672757214000},"page":"1171-1187","source":"Crossref","is-referenced-by-count":4,"title":["AcneTyper: An automatic diagnosis method of dermoscopic acne image via self-ensemble and stacking"],"prefix":"10.1177","volume":"31","author":[{"given":"Shuai","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruili","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Venereology, The First Hospital of Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Venereology, The First Hospital of Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoxiong","family":"Su","sequence":"additional","affiliation":[{"name":"Beijing Dr. of Acne Medical Research Institute, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjiao","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Information Technology (Smart Agriculture Research Institute), Jilin Agricultural University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lan","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengfeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/THC-220295_ref2","doi-asserted-by":"crossref","unstructured":"Lozynska K, Glabska D. Association between nutritional behaviours and acne-related quality of life in a population of polish male adolescents. Nutrients. 2022; 14(13).","DOI":"10.3390\/nu14132677"},{"issue":"5","key":"10.3233\/THC-220295_ref3","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.ijwd.2020.07.013","article-title":"Safety and efficacy of a novel three-step anti-acne regimen formulated specifically for women","volume":"6","author":"Falla","year":"2020","journal-title":"Int J Womens Dermatol."},{"issue":"8","key":"10.3233\/THC-220295_ref9","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1166\/jmihi.2017.2279","article-title":"Automated system for acne vulgaris grading using self-organizing map","volume":"7","author":"Khan","year":"2017","journal-title":"Journal of Medical Imaging and Health Informatics."},{"issue":"4","key":"10.3233\/THC-220295_ref10","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1007\/s11053-019-09465-w","article-title":"Novel hybrid integration approach of bagging-based fisher\u2019s linear discriminant function for groundwater potential analysis","volume":"28","author":"Chen","year":"2019","journal-title":"Natural Resources Research."},{"issue":"2","key":"10.3233\/THC-220295_ref16","doi-asserted-by":"crossref","first-page":"47","DOI":"10.14801\/JAITC.2018.8.2.47","article-title":"Skin condition analysis of facial image using smart device: Based on acne, pigmentation, flush and blemish","volume":"8","author":"Park","year":"2018","journal-title":"Journal of Advanced Information Technology and Convergence."},{"issue":"1","key":"10.3233\/THC-220295_ref20","doi-asserted-by":"crossref","first-page":"5839","DOI":"10.1038\/s41598-018-24204-6","article-title":"An automatic diagnosis method of facial acne vulgaris based on convolutional neural network","volume":"8","author":"Shen","year":"2018","journal-title":"Scientific Reports."},{"key":"10.3233\/THC-220295_ref25","doi-asserted-by":"crossref","unstructured":"Isa NAM, Mangshor NNA. Acne Type Recognition for Mobile-Based Application Using YOLO. Journal of Physics: Conference Series. 2021; 1962(1): 012041.","DOI":"10.1088\/1742-6596\/1962\/1\/012041"},{"key":"10.3233\/THC-220295_ref26","doi-asserted-by":"crossref","first-page":"104610","DOI":"10.1016\/j.compbiomed.2021.104610","article-title":"A smart LED therapy device with an automatic facial acne vulgaris diagnosis based on deep learning and internet of things application","volume":"136","author":"Phan","year":"2021","journal-title":"Computers in Biology and Medicine."},{"issue":"5","key":"10.3233\/THC-220295_ref27","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1093\/bioinformatics\/btz763","article-title":"Feature selection may improve deep neural networks for the bioinformatics problems","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics."},{"key":"10.3233\/THC-220295_ref29","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.ins.2021.07.024","article-title":"BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification","volume":"577","author":"Abdar","year":"2021","journal-title":"Information Sciences."},{"issue":"3","key":"10.3233\/THC-220295_ref30","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1109\/TNB.2018.2842219","article-title":"XGBFEMF: An XGBoost-based framework for essential protein prediction","volume":"17","author":"Zhong","year":"2018","journal-title":"IEEE Transactions on Nanobioscience."},{"key":"10.3233\/THC-220295_ref32","doi-asserted-by":"crossref","unstructured":"Qiu T, Wen C, Xie K, Wen F-Q, Sheng G-Q, Tang X-G. Efficient medical image enhancement based on CNN-FBB model. IET Image Processing. 2019; 13(10): 1736-44.","DOI":"10.1049\/iet-ipr.2018.6380"},{"issue":"7","key":"10.3233\/THC-220295_ref33","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/TPAMI.2018.2846566","article-title":"Fine-tuning CNN image retrieval with no human annotation","volume":"41","author":"Radenovi\u0107","year":"2018","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"key":"10.3233\/THC-220295_ref38","doi-asserted-by":"crossref","unstructured":"Zhang Y, Zhang Y, Xiong Y, Wang H, Deng Z, Song J, et al. T4SEfinder: A bioinformatics tool for genome-scale prediction of bacterial type IV secreted effectors using pre-trained protein language model. Brief Bioinform. 2022; 23(1).","DOI":"10.1093\/bib\/bbab420"},{"key":"10.3233\/THC-220295_ref39","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jclinepi.2017.12.006","article-title":"Linear regression and the normality assumption","volume":"98","author":"Schmidt","year":"2018","journal-title":"Journal of Clinical Epidemiology."},{"key":"10.3233\/THC-220295_ref40","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eswa.2019.05.028","article-title":"A comparison of random forest variable selection methods for classification prediction modeling","volume":"134","author":"Speiser","year":"2019","journal-title":"Expert Systems with Applications."},{"issue":"6","key":"10.3233\/THC-220295_ref41","doi-asserted-by":"crossref","first-page":"2229","DOI":"10.3390\/su12062229","article-title":"Examining hybrid and single SVM models with different kernels to predict rock brittleness","volume":"12","author":"Jahed Armaghani","year":"2020","journal-title":"Sustainability."},{"key":"10.3233\/THC-220295_ref42","doi-asserted-by":"crossref","unstructured":"Liu Y, Gao Y, Fang R, Cao H, Sa J, Wang J, et al. Identifying complex gene-gene interactions: A mixed kernel omnibus testing approach. Brief Bioinform. 2021; 22(6).","DOI":"10.1093\/bib\/bbab305"},{"key":"10.3233\/THC-220295_ref43","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1016\/j.swevo.2018.08.015","article-title":"Ensemble strategies for population-based optimization algorithms \u2013 A survey","volume":"44","author":"Wu","year":"2019","journal-title":"Swarm and Evolutionary Computation."},{"issue":"12","key":"10.3233\/THC-220295_ref44","doi-asserted-by":"crossref","first-page":"e0226910","DOI":"10.1371\/journal.pone.0226910","article-title":"A novel model for malaria prediction based on ensemble algorithms","volume":"14","author":"Wang","year":"2019","journal-title":"PloS One."},{"key":"10.3233\/THC-220295_ref45","doi-asserted-by":"crossref","first-page":"104951","DOI":"10.1016\/j.compbiomed.2021.104951","article-title":"Segmentation technique and dynamic ensemble selection to enhance glaucoma severity detection","volume":"139","author":"Zulfira","year":"2021","journal-title":"Comput Biol Med."},{"key":"10.3233\/THC-220295_ref46","doi-asserted-by":"crossref","first-page":"105725","DOI":"10.1016\/j.compbiomed.2022.105725","article-title":"Automated classification of nasal polyps in endoscopy video-frames using handcrafted and CNN features","volume":"147","author":"Ay","year":"2022","journal-title":"Comput Biol Med."},{"key":"10.3233\/THC-220295_ref47","doi-asserted-by":"crossref","first-page":"105616","DOI":"10.1016\/j.compbiomed.2022.105616","article-title":"Scene-graph-driven semantic feature matching for monocular digestive endoscopy","volume":"146","author":"Yang","year":"2022","journal-title":"Comput Biol Med."},{"issue":"6","key":"10.3233\/THC-220295_ref48","doi-asserted-by":"crossref","first-page":"990","DOI":"10.3390\/electronics9060990","article-title":"A two-step rule-extraction technique for a cnn","volume":"9","author":"Bologna","year":"2020","journal-title":"Electronics."},{"key":"10.3233\/THC-220295_ref50","doi-asserted-by":"crossref","first-page":"104418","DOI":"10.1016\/j.compbiomed.2021.104418","article-title":"Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning","volume":"135","author":"Abdar","year":"2021","journal-title":"Computers in Biology and Medicine."},{"issue":"3","key":"10.3233\/THC-220295_ref52","doi-asserted-by":"crossref","first-page":"633","DOI":"10.3233\/THC-213096","article-title":"Mathematical and deep learning analysis based on tissue dielectric properties at low frequencies predict outcome in human breast cancer","volume":"30","author":"Shawki","year":"2022","journal-title":"Technology and Health Care."},{"issue":"2","key":"10.3233\/THC-220295_ref53","doi-asserted-by":"crossref","first-page":"469","DOI":"10.3233\/THC-212890","article-title":"Segmentation of acetowhite region in uterine cervical image based on deep learning","volume":"30","author":"Liu","year":"2022","journal-title":"Technology and Health Care."},{"issue":"S1","key":"10.3233\/THC-220295_ref54","doi-asserted-by":"crossref","first-page":"475","DOI":"10.3233\/THC-218045","article-title":"Robust deep learning pipeline for PVC beats localization","volume":"29","author":"Petryshak","year":"2021","journal-title":"Technology and Health Care."},{"issue":"1","key":"10.3233\/THC-220295_ref58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-30116-2","article-title":"An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings","volume":"8","author":"Badnjevic","year":"2018","journal-title":"Scientific reports."},{"issue":"1","key":"10.3233\/THC-220295_ref59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12920-018-0333-2","article-title":"Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics","volume":"11","author":"Catic","year":"2018","journal-title":"BMC Medical Genomics."}],"container-title":["Technology and Health Care"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/THC-220295","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:55:21Z","timestamp":1777614921000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/THC-220295"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,30]]},"references-count":30,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/thc-220295","relation":{},"ISSN":["0928-7329","1878-7401"],"issn-type":[{"value":"0928-7329","type":"print"},{"value":"1878-7401","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,30]]}}}