부산대학교 도서관

We evaluated 12 open source machine learning methods for classification of polar map images in cardiac perfusion positron emission tomography (PET) using a dataset consisting of 138 polar maps. Majority of the classifiers showed good accuracy in 10-fold cross-validation (mean accuracy of 0.75–0.88). Accuracy was slightly lower when applied to a separate hold-out dataset (0.70-0.87). From the evaluated classifiers, a support vector machine using a polynomial kernel, Gaussian naive Bayes classifier and a two-dimensional convolutional neural network had stable performance across both the cross-validation and hold-out datasets (accuracy of 0.78, 0.83 and 0.87).