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Bowel Polyp Detection in Capsule Endoscopy Images
tenth-order Zernike moments are shown in Tables 11.1 and 11.2, respectively. The classification results of CWC features are demonstrated in
Table 11.3.
From these three tables, it can be noticed that the proposed color and
shape feature shows much better performance for polyp recognition from
CE images compared to the CWC method when choosing MLP or SVM
as the classifier. This is expected because the proposed color and shape
features hybrid color invariance with shape invariance. Moreover, the proposed feature with fifth-order Zernike moments and a chromaticity histogram shows an encouraging recognition accuracy of 94.20% when using
MLP as the classifier, together with a promising specificity (93.33%) and
sensitivity (95.07%). An unexpected result is that the performance of an
SVM is inferior to that of an MLP for the proposed color and shape features. Such a result may be due to the fact that the parameters used in SVM
experiments are not optimized.
TABLE 11.1
Classification Results of the Proposed Algorithm with Fifth-Order
Zernike Moment and the Chromaticity Histogram (%)
MLP
SVM
Accuracy
94.20
85.33
Specificity
93.33
80.67
Sensitivity
95.07
90.00
TABLE 11.2
Classification Results of the Proposed Algorithm with Tenth-Order
Zernike Moment and the Chromaticity Histogram (%)
MLP
SVM
Accuracy
93.47
89.00
Specificity
93.53
81.34
Sensitivity
93.42
96.67
TABLE 11.3
Classification Results of CWC Method (%)
MLP
SVM
Accuracy
58.67
70.50
Specificity
63.67
63.67
Sensitivity
53.67
76.33