| Autor: |
K., Punithavathy, Poobal, Sumathi, Ramya, M. M. |
| Předmět: |
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| Zdroj: |
Indian Journal of Public Health Research & Development; Oct2018, Vol. 9 Issue 10, p1126-1132, 7p |
| Abstrakt: |
Early lung cancer detection is extremely challenging as symptoms are not exposed till advanced stage. This study is aimed at developing a computer aided diagnosis (CAD) system with image processing techniques and support vector machine (SVM) in lung cancer classification from positron emission tomography/computed tomography (PET/CT) images. The developed CAD system utilized fuzzy enhancement for contrast improvement. Texture and fractal features were used for training the SVM. This study utilized 82 PET/CT images and 10-fold cross validation to analyze the performance of the classifiers. Experimental study showed that SVM classifier with radial basis function (RBF) kernel of width, s = 1 outperformed the other SVM models. It produced maximum accuracy of 98.13% using texture and fractal features from PET/CT images. The RBF kernel is effective in handling sparse, non-linear, multi-dimensional data to transform it into linearly separable. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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