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Table 3 Comparison of the performance of wavelet bases on the MIAS dataset

From: Automatic detection of anomalies in screening mammograms

Wavelet basis§ Best feature combination Sensitivity*(%) Specificity(%) Overallclassification rate (%)
Haar S-h1 K-a2 K-a8 90.8 32.7 51.5
Db2 K-a3    93.9 14.1 39.9
Db4 K-h5    94.9 9.3 37.0
Db8 S-h3 S-a4 K-d4 91.8 27.3 48.2
Bior 1.5 K-h3 K-a1 K-a8 94.9 13.7 39.9
Bior 2.2 K-a2    94.9 14.1 40.3
Bior 2.8 S-d5 K-a4   94.9 27.3 49.2
Bior 3.7 S-d6 K-d8 K-a7 93.9 23.9 46.5
Bior 4.4 K-h6 K-a2 K-a5 93.9 16.1 41.3
Bior 5.5 K-a1    93.9 14.1 39.9
Bior 6.8 S-h3 K-h7 K-a3 94.9 22.0 45.5
  1. §Wavelet basis notation: Dbn where n describes the number of coefficients used in the wavelet. Db2 encodes polynomials with two coefficients, i.e. constant and linear components. Biorm.n where n describes the order for decompositiona and m is the order used for reconstruction.
  2. *Sensitivity is defined as TP/(TP + FN).
  3. †Specificity is defined as TN/(TN + FP).
  4. ‡Classification rate is defined as (TP + TN)/(TP + TN + FP + FN).
  5. The best triplet feature was selected for each wavelet.