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dc.contributor.authorMueller, Willen
dc.date.accessioned2020-08-31T21:54:39Z
dc.date.available2020-08-31T21:54:39Z
dc.identifier.urihttp://hdl.handle.net/1974/28076
dc.description.abstractWe present a novel skin quality assessment system with the goal of improving skin care. This system leverages digital dermoscopy and consists of hardware, software, and a machine learning component to automate the process of skin quality assessment. We first give a brief introduction to the problem followed by and in-depth overview of skin assessment and related works. Following this, the different components of the system are described along with the data collection and algorithms. We performed two studies with 114 participants in total. These studies yielded 2332 skin images that were ranked by experts across a number of skin quality metrics. We plan to release this dataset as part of this work. Using this data we trained several classical and deep learning algorithms to classify Dryness, Frailty, and existence of hair for the images. The best performance for Dryness detection was achieved by VGG16 with an accuracy of 0.8606, while Frailty was best classified by VGG19 with an accuracy of 0.7929. Additionally, Hair was best detected by VGG16 with an accuracy of 0.8756 We believe our proposed and developed system creates a new research direction in the area of digital mobile-based dermoscopy, showing the feasibility of low-cost, mobile, and reliable skin quality assessment.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectSkin Qualityen
dc.subjectDermoscopyen
dc.subjectDeep Learningen
dc.subjectMachine Learningen
dc.subjectFrailtyen
dc.subjectDryen
dc.subjectHairen
dc.titleDeepSkin: Mobile-based Skin Quality Assessment with Deep Learningen
dc.typethesisen
dc.description.degreeM.A.Sc.en
dc.contributor.supervisorEtemad, Ali
dc.contributor.supervisorWoo, Kevin
dc.contributor.departmentElectrical and Computer Engineeringen
dc.degree.grantorQueen's University at Kingstonen


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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
Except where otherwise noted, this item's license is described as Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada