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On the other hand, other approaches such as [ 11, 23] have already demonstrated that MS is a good source of information to detect pathological voices or to perform an automatic pathology classification.
In recent years, a number of methods based on acoustic analysis were developed for vocal fold pathology classification [5-7].
This review article aims to provide knowledge on the epidemiology, pathology, classification, history/assessment and early management of burns in children.
Therefore, using the reduced feature vector leads to the decreasing of the response time of the program of vocal fold pathology classification in comparison with the non-reduced feature vector.
The fourth experiment is carried out to compare the response time of the vocal fold pathology classification system based on the initial feature vector and the reduced feature vector.
Some of the previous works [9-12,17,20,21,23,24,27,29,32-53] for the vocal fold pathology classification problem have been analyzed from the dataset and classifier points of view [see Additional file 1].
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Changes in pathology classifications over the years may account in part for these discrepancies.
The single and multiple pathology classifications were then used to predict longitudinal change in each of the cognitive outcomes and in two derived composites.
As shown in Figure 5, the attenuation coefficients for most of the pathology classifications fall within the median absolute deviation range for the normal breast tissue (gray band).
In the 10 pathology types where the peak was present, the amplitude of the peak varied significantly from specimen to specimen, and it therefore could not be used to discriminate between the 10 pathology classifications.
Response to therapy is dependent on the pathology and classification of the breast tumour.
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