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Classification of Osteosarcoma Tray Responses Using Adaptive and Rational Wavelets for Feature Extraction
| Content Provider | Semantic Scholar |
|---|---|
| Author | Ng, Desmond Tian, Wanxin Withayachumnankul, Withawat Findlay, David Ferguson, Bradley Abbott, Derek |
| Copyright Year | 2008 |
| Abstract | In this work we investigate new feature extraction algorithms on the T-ray response of normal human bone cells and human osteosarcoma cells. One of the most promising feature extraction methods is the Discrete Wavelet Transform (DWT). However, the classification accuracy is dependant on the specific wavelet base chosen. Adaptive wavelets circumvent this problem by gradually adapting to the signal to retain optimum discriminatory information, while removing redundant information. Using adaptive wavelets, classification accuracy, using a quadratic Bayesian classifier, of 96.88% is obtained based on 25 features. In addition, the potential of using rational wavelets rather than the standard dyadic wavelets in classification is explored. The advantage it has over dyadic wavelets is that it allows a better adaptation of the scale factor according to the signal. An accuracy of 91.15% is obtained through rational wavelets with 12 coefficients using a Support Vector Machine (SVM) as the classifier. These results highlight adaptive and rational wavelets as an efficient feature extraction method and the enormous potential of T-rays in cancer detection. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.eleceng.adelaide.edu.au/personal/dabbott/conference/SPI_ng2008a.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Acclimatization Adaptive filter Adaptive grammar Algorithm Choose (action) Coefficient Discrete wavelet transform Dyadic transformation Eisenstein's criterion Embedded system Embedding Feature extraction Kullback–Leibler divergence Linear discriminant analysis Naive Bayes classifier Network packet Osteosarcoma of bone Packet analyzer Radiation Support vector machine |
| Content Type | Text |
| Resource Type | Article |
