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Kharrazi et al. [9] achieved 98.73% accuracy by using statistical features (average pixel values, wavelet domain statistics).
By using statistical features and wavelet energy features as the input features, the proposed PPR model provides more simple training procedure and better generalization performance than using single recognizer, and hence is easier to be used by quality engineers and operators.
Perhaps the most important deficiency common to both methods arises because cutoffs for abnormal fetal growth are determined by using statistical features of the weight distribution (e.g., the 3rd or 10th percentile).
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In the early stage, researchers focus on using statistical features to classify texture images.
Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods.
The performance of SAE features is also compared with commonly used statistical features.
Using the structural features, we have achieved as high as 12.63% better prediction accuracy than traditionally used statistical features.
If not otherwise provided, the algorithm starts by selecting a reasonable root feature using statistical methods and dataset resampling (using the subroutine RootSelection, described in more detail below).
Some useful features were extracted using statistical measures.
The individual and combined results produced by textural and morphological features are analyzed using statistical parameters: accuracy, sensitivity, specificity, MCC and area under ROC curve.
The extracted SE features are ranked using statistical t test analysis and the dimensionality of the ranked feature vector is reduced by applying PCA.
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