Making decisions on brain tumor diagnosis by soft computing techniques

In this paper, a synergy of advanced signal processing and soft computing strategies is applied in order to identify different types of human brain tumors, as a help to confirm the histological diagnosis of experts and consequently to facilitate the decision about the correct treatment or the necessity of an operation. A computational tool has been developed that merges, on the one hand, wavelet transform to reduce the size of the biomedical spectra and to extract the main features, and on the other hand, Support Vector Machine and Neural Networks to classify them. The influence of some of the configuration parameters of each of those soft computing techniques on the clustering is analyzed. These two methods and another one based on medical knowledge are compared. The classification results obtained by these computational tools are promising specially taking into account that medical knowledge has not been considered and that the number of samples of each class is very low in some cases.

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