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Diagnosis of rosacea is principally done by a physical examination and a consistent patient history. However, qualitative human assessment is often subjective and suffers from a relatively high intra\u2010 and inter\u2010observer variability in evaluating patient outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>To overcome these problems, we propose a quantitative and reproducible computer\u2010aided diagnosis system, Ros\u2010NET, which integrates information from different image scales and resolutions in order to identify rosacea lesions. This involves adaption of Inception\u2010ResNet\u2010v2 and ResNet\u2010101 to extract rosacea features from facial images. Additionally, we propose to refine the detection results by means of <jats:italic>facial\u2010landmarks\u2013based zones<\/jats:italic> (ie, anthropometric landmarks) as regions of interest (ROI), which focus on typical areas of rosacea occurrence on a face.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Using a leave\u2010one\u2010patient\u2010out cross\u2010validation scheme, the weighted average Dice coefficients, in percentages, across all patients (N\u00a0=\u00a041) with 256\u00a0\u00d7\u00a0256 image patches are 89.8\u00a0\u00b1\u00a02.6% and 87.8\u00a0\u00b1\u00a02.4% with Inception\u2010ResNet\u2010v2 and ResNet\u2010101, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The findings from this study support that pre\u2010trained networks trained via transfer learning can be beneficial in identifying rosacea lesions. Our future work will involve expanding the work to a larger database of cases with varying degrees of disease characteristics.<\/jats:p><\/jats:sec>","DOI":"10.1111\/srt.12817","type":"journal-article","created":{"date-parts":[[2019,12,18]],"date-time":"2019-12-18T05:51:12Z","timestamp":1576648272000},"page":"413-421","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Ros\u2010NET: A deep convolutional neural network for automatic identification of rosacea lesions"],"prefix":"10.1111","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2537-8280","authenticated-orcid":false,"given":"Hamidullah","family":"Binol","sequence":"first","affiliation":[{"name":"Center for Biomedical Informatics Wake Forest School of Medicine Winston\u2010Salem NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alisha","family":"Plotner","sequence":"additional","affiliation":[{"name":"Department of Dermatology The Ohio State University Columbus OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Sopkovich","sequence":"additional","affiliation":[{"name":"Department of Dermatology The Ohio State University Columbus OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8079-0011","authenticated-orcid":false,"given":"Benjamin","family":"Kaffenberger","sequence":"additional","affiliation":[{"name":"Department of Dermatology The Ohio State University Columbus OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1278-7512","authenticated-orcid":false,"given":"Muhammad Khalid Khan","family":"Niazi","sequence":"additional","affiliation":[{"name":"Center for Biomedical Informatics Wake Forest School of Medicine Winston\u2010Salem NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2421-8229","authenticated-orcid":false,"given":"Metin N.","family":"Gurcan","sequence":"additional","affiliation":[{"name":"Center for Biomedical Informatics Wake Forest School of Medicine Winston\u2010Salem NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,12,17]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2014.08.028"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2017.08.037"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/archderm.1994.01690030091015"},{"issue":"6","key":"e_1_2_11_5_1","first-page":"607","article-title":"Automated Detection of Facial Disorders (ADFD): a novel approach based\u2010on digital photographs","volume":"6","author":"Goceri E","year":"2018","journal-title":"Comput Methods Biomech Biomed Eng Imaging Vis"},{"key":"e_1_2_11_6_1","doi-asserted-by":"crossref","unstructured":"RamliR MalikAS HaniAFM JamiA.Segmentation of acne vulgaris lesions. in 2011 International Conference on Digital Image Computing: Techniques and Applications.2011. 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