Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

5 Results
Pit Pattern Type I II III-L III-S IV V
k-NN
I 49 0 5 0 45 0
II 36 0 14 0 49 0
III-L 30 0 42 0 27 0
III-S 25 0 50 0 25 0
IV 33 0 14 0 51 0
V 40 0 8 0 52 0
SVM
I 56 11 14 5 12 2
II 33 37 2 4 19 5
III-L 27 5 27 8 27 5
III-S 25 0 50 8 8 8
IV 27 13 13 2 40 4
V 36 12 12 0 24 16
Table 5.9: Result distribution matrices for BB for 6 classes (Pit pattern images)
tractor “Subband energy”. The feature vector dimensions are quite different among the
classifiers used, with s = 1, 1 for the k-NN classifier and s = 8, 41 for the SVM classifier
(note that these values for s correspond to the number of subbands used to compute features
for the two classes case and the six classes case, respectively).
Additionally the k-value for the k-NN classifier differs very much between the two classes
case and the six classes case, with k = 14 and k = 50, respectively.
Figure 5.3 shows the results obtained for different choices for s and k in the six classes
case using pit pattern images and the k-NN classifier.
While figure 5.3(a) shows the classification results for all classes, figures 5.3(b)-(g) show
the classification results for the separate classes. In these figures the color shows the classification
result for a specific combination of s and k, where a black pixel denotes a classification
rate of 0% and yellow denotes a classifcation result of 100%.
As we can see from 5.3(a) the best overall classification results are obtained by using a
small value for s, while the choice of k does not play such an important role. As already
pointed out above, we can clearly see that for pit pattern types II, III-S and V the classification
accuracy is very low no matter what choice we make for s and k. For all other types we
clearly obtain better classification results.
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