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Data mining framework for fatty liver disease classification in ultrasound: A hybrid feature extraction paradigm
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10.1118/1.4725759
/content/aapm/journal/medphys/39/7/10.1118/1.4725759
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/39/7/10.1118/1.4725759

Figures

Image of FIG. 1.
FIG. 1.

Block diagram of the proposed Symtosis system for fatty liver disease detection; the blocks outside the dotted shaded rectangular box represent the flow of offline training system, and the blocks within the dotted box represent the online real-time system.

Image of FIG. 2.
FIG. 2.

Normal liver images (left column) and abnormal liver images (right column).

Image of FIG. 3.
FIG. 3.

Principal domain region (Ω) used for the computation of the bispectrum for real signals.

Image of FIG. 4.
FIG. 4.

DWT decomposition.

Image of FIG. 5.
FIG. 5.

ROC curves of the DT and Fuzzy classifiers using Symtosis.

Tables

Generic image for table
TABLE I.

Mean ± standard deviation (SD) values of the significant features for the normal and abnormal classes using Symtosis system.

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TABLE II.

Symtosis classification results (the listed values are average of values obtained in the three folds) TN: true negatives, FN: false negatives, TP: true positives, FP: false positives, A: accuracy, PPV: positive predictive value, Sn: sensitivity, Sp: specificity.

Generic image for table
TABLE III.

Summary of studies that presented various CAD techniques for liver image classification.

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/content/aapm/journal/medphys/39/7/10.1118/1.4725759
2012-06-22
2014-04-21
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Data mining framework for fatty liver disease classification in ultrasound: A hybrid feature extraction paradigm
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/39/7/10.1118/1.4725759
10.1118/1.4725759
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