study hopes to establish a numerical analysis system for mitochondrial morphology of β-cells .....................................................................36 Fig 4.2 Classification tree of system for protein overexpression dataset segmented
AC, Abakay A, Temurtas F (2012). An approach based on probabilistic neuralnetwork for )5.44 ± 4.72White bloodContinuousNumber of white blood cellsin the pleural fluid per
proposed a minority sensitive decision tree algorithm (MiDT) for imbalanced data classification . Velmurugan, “Performance analysis of decision tree algorithms for breast cancer classification
(classification). CART produces decision trees that can be represented by a collection of rules based generate the optimal value of features for pursuing higher classification performance. In the
. The flow chart of the automatic template-based breast segmentation for a slice is shown in Features The blood vessels is a pipeline for delivering nutrients to tumor (44). In order to
forest classifier to vote on features of slices; and MCSNNs, a multi-view CNN algorithm to features with a softmax top-most output layer for the task-specific classification. See Figure
. Based on the decision tree model, a warning light system is created to predict the situation ) and Gini index We utilize decision tree (DT) [11-14] for classification of Cushing
............................................................................................ 45 Figure 4-5 The implementation of summations is based on binary adder tree engine on the CUDA-based GPU platform. The block (a) (b) Fig. 1.2 (a) Normal image
as a convolving process of the image with filters that searches for geometric structures ] developed a 3D approach for analyzing vascular structure precisely, but the performance of this
reported achieving a 93.1 1 % classification accuracy with HOS-based features [48,49]. Other a chaos analysis based on the wavelet decomposition of EEG signals of healthy subjects
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