Classification of biomedical data of thermoacoustic tomography to detect physiological abnormalities in the body tissues

Based on thermal acoustic data from the body tissue in upper arm that has been produced through thermal acoustic tomography method, classification system for the data has been built as a support of decision making about physiological abnormality. The advantages of the system built in this research is able to detect physiological abnormalities in the body tissues without the need for surgery (non-invasive method), such as to detect the existence of tumor or cancer and the damage of kidney or liver. Two classification methods has been evaluated for comparison, which are Backpropagation Neural Network (BP-NN) and Support Vector Machine (SVM). Using SVM that based on boundary optimization and BP-NN that based on error minimization as comparison, it is expected to obtain the most suitable classification method for decision making about physiological abnormalities in body tissues. The experimental results on 60 train data and 40 test data show that the accuracy for classification using Backpropagation Neural Network and Support Vector Machine (SVM) are 70% and 67.5%, respectively. In general, it can be proved that classification using Backpropagation Neural Network takes a longer running time (around 0.9 seconds) than classification using SVM (around 0.05 seconds).