Performance Improvement of Automatic Basal Cell Carcinoma Detection Using Half Hanning Window

Half Hanning 윈도우 전처리를 통한 기저 세포암 자동 검출 성능 개선

  • 박아론 (전남대학교 전자컴퓨터공학부) ;
  • 백성준 (전남대학교 전자컴퓨터공학부) ;
  • 민소희 (전남대학교 전자컴퓨터공학부) ;
  • 유홍연 (전남대학교 전자컴퓨터공학부) ;
  • 김진영 (전남대학교 전자컴퓨터공학부) ;
  • 홍성훈 (전남대학교 전자컴퓨터공학부)
  • Published : 2006.12.28


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 negative ratio. Classification results with various classifiers are presented to show the effectiveness of the proposed method. The classifiers include maximum a posteriori probability (MAP), k-nearest neighbor (KNN), probabilistic neural network (PNN), multilayer perceptron(MLP), support vector machine (SVM) and minimum squared error (MSE) classification. Classification results with KNN involving 216 spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic BCC detection.


BCC;Raman Spectrum;Pattern Recognition