Computer-aided diagnosis of skin lesions using conventional digital photography: A reliability and feasibility study

NCUIR > college of Health Sciences and Technology > Department of Biomedical Sciences and Engineering > journal & Dissertation > Item 987654321/102712

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/102712

Title:	Computer-aided diagnosis of skin lesions using conventional digital photography: A reliability and feasibility study
Authors:	黃輝揚;Chang, Wen-Yu;Huang, Adam;Yang, Chung-Yi;Lee, Chien-Hung;Chen, Yin-Chun;Wu, Tian-Yau;Chen, Gwo-Shing
Contributors:	生醫理工學院生醫科學與工程學系
Keywords:	Accuracy;Adult;Aged;Artificial intelligence;Biomarkers;Biomedical engineering;Cancer;Classification;Color;Correlation analysis;Data analysis;Dermatology;Diagnosis;Digital computers;Digital imaging;Digital photography;Feasibility Studies;Feature extraction;Female;Hospitals;Humans;Image classification;Image Interpretation, Computer-Assisted;Image processing;Informed consent;Lesions;Male;Medical diagnosis;Medical personnel;Medical screening;Medicine;Melanoma;Middle Aged;Pattern recognition;Photography;Photomacrographs;Physicians;Principal Component Analysis;Principal components analysis;Reproducibility of Results;ROC Curve;Sensitivity;Skin;Skin cancer;Skin diseases;Skin Neoplasms - diagnosis;Software;Software reliability;Support Vector Machine;Support vector machines;Survival analysis;Tumors
Date:	2013-11-04
Issue Date:	2026-04-23 11:15:25 (UTC+8)
Publisher:	Public Library of Science;United States: Public Library of Science (PLoS)
Abstract:	摘要： Computer-aided diagnosis (CADx) software that provides a second opinion has been widely used to assist physicians with various tasks. In dermatology, however, CADx has been mostly limited to melanoma or melanocytic skin cancer diagnosis. The frequency of non-melanocytic skin cancers and the accessibility of regular digital macrographs have raised interest in developing CADx for broader applications. To investigate the feasibility of using CADx to diagnose both melanocytic and non-melanocytic skin lesions based on conventional digital photographic images. This study was approved by an institutional review board, and the requirement to obtain informed consent was waived. In total, 769 conventional photographs of melanocytic and non-melanocytic skin lesions were retrospectively reviewed and used to develop a CADx system. Conventional and new color-related image features were developed to classify the lesions as benign or malignant using support vector machines (SVMs). The performance of CADx was compared with that of dermatologists. The clinicians' overall sensitivity, specificity, and accuracy were 83.33%, 85.88%, and 85.31%, respectively. New color correlation and principal component analysis (PCA) features improved the classification ability of the baseline CADx (p = 0.001). The estimated area under the receiver operating characteristic (ROC) curve (Az) of the proposed CADx system was 0.949, with a sensitivity and specificity of 85.63% and 87.65%, respectively, and a maximum accuracy of 90.64%. We have developed an effective CADx system to classify both melanocytic and non-melanocytic skin lesions using conventional digital macrographs. The system's performance was similar to that of dermatologists at our institute. Through improved feature extraction and SVM analysis, we found that conventional digital macrographs were feasible for providing useful information for CADx applications. The new color-related features significantly improved CADx applications for skin cancer. 其他題名： PLoS One 出版者： United States: Public Library of Science (PLoS) 出版日期： 2013-11-04 出處： PLoS ONE, 2013-11, Vol.8 (11), p.e76212- 資源來源： Agricultural & Environmental Science Collection 版權： COPYRIGHT 2013 Public Library of Science 版權： 2013 Chang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 版權： 2013 Chang et al 2013 Chang et al 識別號： ISSN: 1932-6203 識別號： EISSN: 1932-6203 識別號： DOI: 10.1371/journal.pone.0076212 識別號： PMID: 24223698
Appears in Collections:	[Department of Biomedical Sciences and Engineering ] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	20	View/Open

社群 sharing

Loading...