ROC curves for the Feature Network (a), the Topographic Map Network (b), the Short-time Fourier Transform Network (c), The Long Short-term Memory Network (d), and the SFN (e). The ROC curves are shown for each individual class (colored) and the micro-average (black). The black dotted line represents the ROC of a random classifier and is provided as a visual comparison.</p
(a) shows the liver data and (b) shows the kidney data. In the plot, BR plus LR represents BR model ...
(a) ROC curve before (yellow) and after (blue) augmentation for the classifier based on unbalanced d...
<p>In blue, performances for linear support machines using the combined subset classifier approach (...
<p>The Figure shows receiver operator characteristic (ROC) and precision to recall curves (PR) for n...
The relative operating characteristic (ROC) method is applied to performance evaluation of neural ne...
<p><b>(a)</b> The ROC curves and AUC values of six temporal sequence networks: AlexNet-LSTM, GoogLeN...
<p>The ROC and PR curves (Ensemble, AIC, BIC, MAX_AUPR and MAX_AUROC) are vertical averages of the c...
ROC curve comparing 6 machine learning algorithms for A) train and B) test data.</p
LDA feature selection (a) selects 192 features with most being relative PSD band features and standa...
The relative operating characteristic (ROC) method is applied to performance evaluation of neural ne...
The column of the model utilizing only clinical data from our previous study [6] is included for com...
(A) AUPR measures for different types of features. Statistical tests were performed as described in ...
Dots with error bars represent out-of-fold performance on the training data, and triangles represent...
Dots with error bars represent out-of-fold performance on the training data, and triangles represent...
ROC curves for the top performing model compared to individual feature predictions.</p
(a) shows the liver data and (b) shows the kidney data. In the plot, BR plus LR represents BR model ...
(a) ROC curve before (yellow) and after (blue) augmentation for the classifier based on unbalanced d...
<p>In blue, performances for linear support machines using the combined subset classifier approach (...
<p>The Figure shows receiver operator characteristic (ROC) and precision to recall curves (PR) for n...
The relative operating characteristic (ROC) method is applied to performance evaluation of neural ne...
<p><b>(a)</b> The ROC curves and AUC values of six temporal sequence networks: AlexNet-LSTM, GoogLeN...
<p>The ROC and PR curves (Ensemble, AIC, BIC, MAX_AUPR and MAX_AUROC) are vertical averages of the c...
ROC curve comparing 6 machine learning algorithms for A) train and B) test data.</p
LDA feature selection (a) selects 192 features with most being relative PSD band features and standa...
The relative operating characteristic (ROC) method is applied to performance evaluation of neural ne...
The column of the model utilizing only clinical data from our previous study [6] is included for com...
(A) AUPR measures for different types of features. Statistical tests were performed as described in ...
Dots with error bars represent out-of-fold performance on the training data, and triangles represent...
Dots with error bars represent out-of-fold performance on the training data, and triangles represent...
ROC curves for the top performing model compared to individual feature predictions.</p
(a) shows the liver data and (b) shows the kidney data. In the plot, BR plus LR represents BR model ...
(a) ROC curve before (yellow) and after (blue) augmentation for the classifier based on unbalanced d...
<p>In blue, performances for linear support machines using the combined subset classifier approach (...