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Image Search Results
Journal: Journal of Clinical Medicine
Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects
doi: 10.3390/jcm10225330
Figure Lengend Snippet: All tested classifiers ranked in terms of accuracy (%).
Article Snippet: In detail:
Techniques: Plasmid Preparation
Journal: Journal of Clinical Medicine
Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects
doi: 10.3390/jcm10225330
Figure Lengend Snippet: Performance of the trained optimizable k-nearest neighbor (KNN) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) with the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.
Article Snippet: In detail:
Techniques:
Journal: Journal of Clinical Medicine
Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects
doi: 10.3390/jcm10225330
Figure Lengend Snippet: Performance of the trained optimizable support vector machine (SVM) classifier. ( a ) Confusion matrix showing the true and predicted classes of the ToF patients’ videos. The blue diagonal is related to the number of videos that were truly recognized (Predicted Class = True Class), whereas the pink antidiagonal is related to the number of videos that were falsely recognized (Predicted Class ≠ True Class). ( b ) Same as ( a ) the True Positive Rate (TPR) in blue and the False Negative Rate (FNR) in pink.
Article Snippet: In detail:
Techniques: Plasmid Preparation
Journal: Journal of Clinical Medicine
Article Title: Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects
doi: 10.3390/jcm10225330
Figure Lengend Snippet: Optimization of the k-nearest neighbor (KNN) classifier. ( a ) The classifier was optimized over 100 iterations via the minimization of the classification error and its optimized hyperparameters are reported as “Optimization Results”. In detail, for each iteration of the optimization, the classifier was also 10-fold cross-validated. ( b ) Receiver Operating Characteristic (ROC) curve with the area under the curve (AUC) painted in blue; a value of AUC close to 1 means a very low classification error for the optimized classifier.
Article Snippet: In detail:
Techniques: