Adaptive simulated annealing for CT image classification

We present a pattern classification method that combines the classical Perceptron algorithm with simulated annealing. For a sample set S of n-dimensional patterns labeled as positive and negative, our algorithm computes threshold circuits of small depth where the linear threshold functions of the first layer are calculated by simulated annealing with the logarithmic cooling schedule c(k) = Gamma(k) / 1n (k + 2). The parameter F depends on the sample set and changes in time, and the neighborhood relation is determined by the Perceptron algorithm. We apply the approach to the recognition of focal liver tumours. From 400 positive (focal liver tumour) and 400 negative (normal liver tissue) examples a depth-six threshold circuit is calculated. The examples axe of size n = 14 161 = 119 x 119 and they are presented in the DICOM format. On test sets of 100+100 examples (disjoint from the learning set) we obtain a correct classification of more than 98%.