<p>The classification performance is compared between our proposed method (four-layer network features as in Method I) and the conventional volumetric method (Method IV) on 20 training/test groups. Each group contains 150 training samples and 75 test samples randomly partitioned from our data set.</p
<p>Three classifiers, Gaussian Naive Bayes (GNB) in panel (a), SVM in panel (b) and sparse MRF in pa...
Comparison of the average classification accuracy of the networks used in DeephESC and DeephESC 2.0....
<p>Comparison of prediction accuracy on four multiclass classification datasets by varying the numbe...
<p>The classification performance is compared between the four-layer network features in Method I an...
<p>Classification comparison using network features and volumetric features with different numbers o...
<p>The classification accuracy is plotted over different number of training samples. For a given num...
Comparison of the classification performance by the proposed network and other methods.</p
Comparison of the detection performance of different classification methods and oversampling methods...
Comparison of the classification accuracies of different algorithms and different feature fusion met...
This research uses four classification algorithms in standard and boosted forms to predict members o...
<p>The classification accuracy is plotted over different number of training samples. For a given num...
<p>Comparison of accuracy rate of different features extracted with classification algorithms.</p
Comparison of the detection performance of different classification methods and oversampling methods...
Summarization: The classification problem is of major importance to a plethora of research fields. T...
<p>For both 2-class and 4-class classification, classifier performs better when trained with feature...
<p>Three classifiers, Gaussian Naive Bayes (GNB) in panel (a), SVM in panel (b) and sparse MRF in pa...
Comparison of the average classification accuracy of the networks used in DeephESC and DeephESC 2.0....
<p>Comparison of prediction accuracy on four multiclass classification datasets by varying the numbe...
<p>The classification performance is compared between the four-layer network features in Method I an...
<p>Classification comparison using network features and volumetric features with different numbers o...
<p>The classification accuracy is plotted over different number of training samples. For a given num...
Comparison of the classification performance by the proposed network and other methods.</p
Comparison of the detection performance of different classification methods and oversampling methods...
Comparison of the classification accuracies of different algorithms and different feature fusion met...
This research uses four classification algorithms in standard and boosted forms to predict members o...
<p>The classification accuracy is plotted over different number of training samples. For a given num...
<p>Comparison of accuracy rate of different features extracted with classification algorithms.</p
Comparison of the detection performance of different classification methods and oversampling methods...
Summarization: The classification problem is of major importance to a plethora of research fields. T...
<p>For both 2-class and 4-class classification, classifier performs better when trained with feature...
<p>Three classifiers, Gaussian Naive Bayes (GNB) in panel (a), SVM in panel (b) and sparse MRF in pa...
Comparison of the average classification accuracy of the networks used in DeephESC and DeephESC 2.0....
<p>Comparison of prediction accuracy on four multiclass classification datasets by varying the numbe...
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