Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

5 Results
Pit Pattern Type I II III-S III-L IV V Total
BEST-BASIS METHOD (BB)
k-NN (2 classes) 65 57 60
k-NN (6 classes) 50 0 0 42 52 0 36
SVM (2 classes) 76 66 70
SVM (6 classes) 56 37 8 27 40 16 39
BEST-BASIS METHOD (STRUCTURAL FEATURES) (BBS)
k-NN (2 classes) 19 87 58
k-NN (6 classes) 28 0 0 0 89 0 35
SVM (2 classes) 78 77 78
SVM (6 classes) 88 9 0 14 91 8 56
BEST-BASIS CENTROIDS (BBCB)
k-NN (2 classes) 66 71 69
k-NN (6 classes) 68 26 0 44 49 0 45
SVM (2 classes) 74 71 72
SVM (6 classes) 52 37 0 44 53 12 56
PYRAMIDAL WAVELET TRANSFORM (WT)
k-NN (2 classes) 54 74 62
k-NN (6 classes) 62 37 0 34 54 0 45
SVM (2 classes) 58 78 70
SVM (6 classes) 47 44 8 34 47 12 41
LOCAL DISCRIMINANT BASES (LDB)
k-NN (2 classes) 49 88 71
k-NN (6 classes) 61 37 0 58 52 0 45
SVM (2 classes) 67 83 76
SVM (6 classes) 47 47 8 47 59 20 48
CENTROID CLASSIFICATION (CC)
2 classes 95 59 74
6 classes 95 0 0 7 52 3 43
CC BASED ON BB AND LDB (CCLDB)
2 classes 74 67 70
6 classes 47 14 33 42 25 48 34
Table 5.3: Percentage of correctly classified images (Pit pattern images)
a percentage of correctly classified images of only 60%. Both, the SVM classifier and the
k-NN classifier show slightly different classification results for both classes. The images
belonging to the non-neoplastic class are clearly better classified by both classifiers.
Regarding the six classes case the best result achieved was 39%, again using the SVM
classifier. The best result for the k-NN classifier again is a bit lower with a classification
66