Detection of Basal Cell Carcinoma Based on Gaussian Prototype Fitting of Confocal Raman Spectra

  • Authors:

  • Seong-Joon Baek;Aaron Park;Sangki Kang;Yonggwan Won;Jin Young Kim;Seung You Na

  • Affiliations:

  • The School of Electronics and Computer Engineering, Chonnam National University, Gwangju, 500-757, South Korea;The School of Electronics and Computer Engineering, Chonnam National University, Gwangju, 500-757, South Korea;Telecommunication R&D Center, Samsung Electronics Co., LTD., 426-791, South Korea;The School of Electronics and Computer Engineering, Chonnam National University, Gwangju, 500-757, South Korea;The School of Electronics and Computer Engineering, Chonnam National University, Gwangju, 500-757, South Korea;The School of Electronics and Computer Engineering, Chonnam National University, Gwangju, 500-757, South Korea

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
  • Year:
  • 2007

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Abstract

Confocal Raman spectroscopy is known to have strong potential for providing noninvasive dermatological diagnosis of skin cancer. According to the previous work, various well known methods including maximum a posteriori probability classifier (MAP), linear classifier using minimum squared error (MSE) and multi layer perceptron networks classifier (MLP) showed competitive results for basal cell carcinoma (BCC) detection. The experimental results are hard to interpret, however, since the classifiers uses global features obtained by principal component analysis (PCA). In this paper, we propose a method that can identify which regions of the spectra are discriminating for BCC detection. For the purpose, 5 and 7 Gaussian prototypes were built located on the typical peak position of BCC and normal (NOR) tissue spectra respectively. Every spectrum is approximated by a linear combination of the Gaussian prototypes. Decision tree is then applied to identify which prototypes are important for the detection of BCC. Among 12 prototypes, 5 discriminating prototypes were selected and the associated weights were used as an input feature vector. According to the experiments involving 216 confocal Raman spectra, support vector machines (SVM) gave 97.4% sensitivity, which confirms that the peak regions corresponding to the selected features are significant for BCC detection and the proposed fitting method is effective.