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

doi:10.1117/12.878124

9 March 2011 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

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 79633O (2011) https://doi.org/10.1117/12.878124
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

Citation Download Citation

Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, and Frederic D. McKenzie "BCC skin cancer diagnosis based on texture analysis techniques", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79633O (9 March 2011); https://doi.org/10.1117/12.878124

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