NDLI: Computer-aided skin cancer screening: Feature selection or feature combination?

Content Provider	IEEE Xplore Digital Library
Author	Situ, Ning Yuan, Xiaojing Wadhawan, Tarun Zouridakis, George
Copyright Year	2010
Description	Author affiliation: Departments of Computer Science, University of Houston, TX 77204, USA (Situ, Ning; Wadhawan, Tarun) \|\| Departments of Engineering Technology, University of Houston, TX 77204, USA (Yuan, Xiaojing; Zouridakis, George)
Abstract	Feature selection is considered as an essential component for a computer-aided skin cancer screening system. Little research has been done on feature combination for automated skin cancer detection while previous systems focused on selecting the most relevant computer generated features. In this paper, we investigate whether combining features can improve the performance of the final decision and compare its performance with that of feature selection. In the biomedical application of skin cancer screening, many categories of descriptors (such as texture, color, and asymmetry) have been used to represent a skin lesion image. The feature combination approach firstly generates classifiers using each category of features alone and combines them linearly together to decide on whether the skin lesion is cancerous or not. Our experimental results show that the performance of the feature combination approach is highly competitive as compared with four widely used feature selection schemes. When further blending the classifiers obtained from feature combination and selection respectively, the fused predictor achieves the best performance.
Starting Page	273
Ending Page	276
File Size	140934
Page Count	4
File Format	PDF
ISBN	9781424479924
ISSN	15224880
e-ISBN	9781424479948
e-ISBN	9781424479931
DOI	10.1109/ICIP.2010.5652821
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-26
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Kernel Image color analysis Skin Histograms Skin cancer Support vector machines image classification Biomedical image processing feature combination feature selection
Content Type	Text
Resource Type	Article

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