Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

5.2 Results
Pit Pattern Type I II III-L III-S IV V
k-NN
I 67 10 7 0 15 0
II 43 26 12 0 17 0
III-L 37 3 44 0 15 0
III-S 33 0 50 0 16 0
IV 33 8 8 0 49 0
V 24 12 8 0 56 0
SVM
I 51 10 16 2 20 0
II 17 36 19 0 22 3
III-L 20 11 44 0 20 3
III-S 25 8 33 0 33 0
IV 7 14 20 1 52 3
V 16 16 8 0 48 12
Table 5.11: Result distribution matrices for BBCB for 6 classes (Pit pattern images)
total classification result was 100% for the k-NN classifier as well as for the SVM classifier.
In the six classes case the classification result was 100% for the k-NN classifier and 100%
for each separate class. The result achieved with the SVM classifier is even better with
an overall classification result of 100% and classification results for each separate class
between 99% and 100%.
The result distribution matrix for the two classes case is a diagonal matrix, containing
100’s only on the main diagonal and zero at all other positions in the matrix. In the six
classes case all the entries on the main diagonal are very close to 100.
According to table 5.6 the best test results for the tests with the pit pattern images were
obtained using the feature extractors “Subband energy” and “Subband variance”. The feature
vector dimensions are s = 32, 23 for the k-NN classifier and s = 45, 29 for the SVM
classifier. Additionally the k-value for the k-NN classifier differs very much between the
two classes case and the six classes case, with k = 3 and k = 33, respectively.
For the test with the Outex images, these parameters are very similar as we can see from
table 5.7. But the feature vector dimensions as well as the k-values for the k-NN classifier
are lower in general. The feature vector dimensions for the classifiers are s = 1, 33 for the
k-NN classifier and s = 4, 31 for the SVM classifier. The k-value for the k-NN classifier are
very similar between the two classes case and the six classes case, with k = 2 and k = 1,
respectively.
In figure 5.5 we see that for the two classes case the overall classification results for pit
pattern images in conjunction with the k-NN classifier are better for choices for k between
1 and 40 and values for s between 22 and 35. But more interesting are the figures for the
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