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

Wen-Yu Chang; Adam Huang; Chung-Yi Yang; Chien-Hung Lee; Yin-Chun Chen; Tian-Yau Wu; Gwo-Shing Chen

doi:10.1371/journal.pone.0076212

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

PLoS One. 2013 Nov 4;8(11):e76212. doi: 10.1371/journal.pone.0076212. eCollection 2013.

Authors

Wen-Yu Chang¹, Adam Huang, Chung-Yi Yang, Chien-Hung Lee, Yin-Chun Chen, Tian-Yau Wu, Gwo-Shing Chen

Affiliation

¹ Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan ; Department of Dermatology, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan.

Abstract

Background: 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.

Objectives: To investigate the feasibility of using CADx to diagnose both melanocytic and non-melanocytic skin lesions based on conventional digital photographic images.

Methods: 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.

Results: 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%.

Conclusions: 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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Aged
Feasibility Studies
Female
Humans
Image Interpretation, Computer-Assisted*
Male
Middle Aged
Photography
Principal Component Analysis
ROC Curve
Reproducibility of Results
Skin Neoplasms / diagnosis*
Software*
Support Vector Machine

Grants and funding

This research was supported by the National Science Council of Republic of China (Taiwan) under Grants NSC 100–2221-E-008–009, 101–2221-E-008–021, NSC 101-2911-I-008–001, and NSC 99–2627-B-008-005. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.