214
Biologically Inspired Robotics
containing any polyp region is labeled as a positive sample; otherwise, it is
labeled as a negative sample. In order to prevent overfitting of the classification results, we exploited threefold cross-validation for all of our classification experiments. To demonstrate the performance of the proposed features,
we compare the proposed scheme with color wavelet covariance (CWC) features used in Karkanis et al. (2003) to detect tumors in traditional endoscopic
images. CWC features are new techniques to represent color features that are
built upon the covariance of second-order textural measures in the wavelet
domain of color channels of images.
We also exploited a multilayer perceptron (MLP) neural network and support
vector machines (SVM) to find a better classifier for our work. A three-layer
MLP with two nonlinear outputs was employed in the experiments. The number of input nodes of the MLP depends on the number of input features, and
the number of epochs for training the MLP was set to 5,000. Because the number of hidden nodes has a strong impact on the final classification results, several variations on the number of hidden layer neurons, ranging from five to
fifty, with five-node increments, were carried out. For SVM implementation,
we referred to the work of Chang and Lin (2001). The radial basis function was
found to be the kernel function that yielded the best classification performance
in our experiments. The highest classification accuracy of an SVM from seven
different sets of the key parameters in an SVM, that is, the penalty parameter
and the kernel parameter, was chosen as the classification performance.
Classification of CE images using MLP or SVM is measured by accuracy,
specificity, and sensitivity, which are widely employed to evaluate the performance of classification. Some definitions are as follows:
Number of Correct Predictions
Accuracy =
Number of o Positives + Number of Negatives
(11.6)
Number of Correct Negative Predict tions
Specificity =
(11.7)
Number of Negatives
Number of Correct Positive Predicti t ons
Sensitivity =
(11.8)
Number of Positives
We performed experiments using different orders of Zernike moments,
that is, fifth- and tenth-order, and the number of these moments corresponds to twelve and thirty-six, respectively, so the number of whole
features is forty and sixty-four, respectively. The average recognition
results of the proposed features using MLP and SVM with fifth-order and
Biologically Inspired Robotics
containing any polyp region is labeled as a positive sample; otherwise, it is
labeled as a negative sample. In order to prevent overfitting of the classification results, we exploited threefold cross-validation for all of our classification experiments. To demonstrate the performance of the proposed features,
we compare the proposed scheme with color wavelet covariance (CWC) features used in Karkanis et al. (2003) to detect tumors in traditional endoscopic
images. CWC features are new techniques to represent color features that are
built upon the covariance of second-order textural measures in the wavelet
domain of color channels of images.
We also exploited a multilayer perceptron (MLP) neural network and support
vector machines (SVM) to find a better classifier for our work. A three-layer
MLP with two nonlinear outputs was employed in the experiments. The number of input nodes of the MLP depends on the number of input features, and
the number of epochs for training the MLP was set to 5,000. Because the number of hidden nodes has a strong impact on the final classification results, several variations on the number of hidden layer neurons, ranging from five to
fifty, with five-node increments, were carried out. For SVM implementation,
we referred to the work of Chang and Lin (2001). The radial basis function was
found to be the kernel function that yielded the best classification performance
in our experiments. The highest classification accuracy of an SVM from seven
different sets of the key parameters in an SVM, that is, the penalty parameter
and the kernel parameter, was chosen as the classification performance.
Classification of CE images using MLP or SVM is measured by accuracy,
specificity, and sensitivity, which are widely employed to evaluate the performance of classification. Some definitions are as follows:
Number of Correct Predictions
Accuracy =
Number of o Positives + Number of Negatives
(11.6)
Number of Correct Negative Predict tions
Specificity =
(11.7)
Number of Negatives
Number of Correct Positive Predicti t ons
Sensitivity =
(11.8)
Number of Positives
We performed experiments using different orders of Zernike moments,
that is, fifth- and tenth-order, and the number of these moments corresponds to twelve and thirty-six, respectively, so the number of whole
features is forty and sixty-four, respectively. The average recognition
results of the proposed features using MLP and SVM with fifth-order and