BCC skin cancer diagnosis based on texture analysis techniques

Shao Hui Chuang, Xiaoyan Sun, Wen Yu Chang, Gwo Shing Chen, Adam Huang, Jiang Li, Frederic D. McKenzie

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% and 95%, respectively.

Original languageEnglish
Title of host publicationMedical Imaging 2011
Subtitle of host publicationComputer-Aided Diagnosis
DOIs
StatePublished - 2011
EventMedical Imaging 2011: Computer-Aided Diagnosis - Lake Buena Vista, FL, United States
Duration: 15 Feb 201117 Feb 2011

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7963
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2011: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityLake Buena Vista, FL
Period15/02/1117/02/11

Keywords

  • BCC Skin Cancer
  • Gray Level Co-occurrence Matrix (GLCM)
  • Gray Level Run Length Matrix (GLCM)
  • Texture Analysis

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