A Machine Learning Based Quantitative Data Analysis for Screening Skin Abnormality Based on Optical Coherence Tomography Angiography (OCTA)

Yubo Ji, Shufan Yang, Kanheng Zhou, Chunhui Li, Zhihong Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Lack of accurate and standard quantitative evaluations limit the progress of applying the OCTA technique into skin clinical trials. More systematic research is required to investigate the possibility of using quantitative OCTA techniques for screening skin diseases. This prospective study included 88 participants (60 normal and 28 abnormal skin samples). In total, 40 OCTA quantitative parameters (3 for epidermis feature, 27 for dermis feature, 10 for vascular feature) were obtained by each OCT and OCTA data volumes. The proposed method relies on linear support vector machines (SVM), while the coefficient of multiple linear regression is also employed to select seven most significant features. Result shows that the proposed method can improve the classification accuracy which can arrive at 93%. Moreover, selected features provide us with direction to determine which biomarker is potential for clinical diagnosis of specific skin abnormalities.

Original languageEnglish
Title of host publicationIEEE International Ultrasonics Symposium, IUS
PublisherIEEE
Number of pages4
ISBN (Electronic)9781665403559
ISBN (Print)9781665447775
DOIs
Publication statusPublished - 13 Nov 2021
Event2021 IEEE International Ultrasonics Symposium, IUS 2021 - Virtual, Online, China
Duration: 11 Sep 201116 Sep 2011

Publication series

NameIEEE International Ultrasonics Symposium, IUS
PublisherInstitute of Electrical and Electronics Engineers
ISSN (Print)1948-5719

Conference

Conference2021 IEEE International Ultrasonics Symposium, IUS 2021
Country/TerritoryChina
CityVirtual, Online
Period11/09/1116/09/11

Keywords

  • Linear Regression
  • Machine Learning
  • OCTA
  • Skin disease
  • SVM

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