Abstract
In this study, we propose a simple preprocessing method for classification of basal cell carcinoma (BCC), which is one of the most common skin cancer. The preprocessing step consists of data clipping with a half hanning window and dimension reduction with principal components analysis (PCA). The application of the half hanning window deemphasizes the peak near 1650cm −1 and improves classification performance by lowering the false positive ratio. Classification results with various classifiers are presented to show the effectiveness of the proposed method. The classifiers include maximum a posteriori (MAP) probability, k-nearest neighbor (KNN), and artificial neural network (ANN) classifier. Classification results with ANN involving 216 confocal Raman spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic BCC detection.
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© 2006 Springer-Verlag Berlin Heidelberg
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Baek, SJ., Park, A. (2006). Basal Cell Carcinoma Detection by Classification of Confocal Raman Spectra. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_84
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DOI: https://doi.org/10.1007/978-3-540-37258-5_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37257-8
Online ISBN: 978-3-540-37258-5
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