Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

5.2 Results
(a) All classes
(b) Non-neoplastic
(c) Neoplastic
Figure 5.6: Results for the BBCB method (2 classes, SVM, pit pattern images)
5.2.4 Pyramidal decomposition (WT)
In contrast to the previous two methods, regarding the tests with the pit pattern images,
this method achieved the best classification result using only the R-channel of the pit pattern
images (i.e. the information stored in the color channels for green and blue has been
discarded).
As we can see in table 5.3, in the two classes case the best result obtained for the pit
pattern images was 70% using the SVM classifier. The best result achieved with the k-NN
classifier was a bit lower, with a percentage of correctly classified images of 62%. Therefore,
with this method, the SVM classifier again outperforms the k-NN classifier. However, it is
interesting that in contrast to the previous two methods the neoplastic images seem to get
significantly more accurately classified than the non-neoplastic ones. This property might
be interesting for future extensions, since by combining this method with another method
which classifies the non-neoplastic images more accurately, we could possibly expect better
overall classification results.
In the six classes case again the SVM classifier outperforms the k-NN classifier with a
classification result of 56% compared to 45% and the misclassification rates for pit pattern
types III-S and V again are very high, just like in the previous method.
77