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Table 2 Comparison of the performance of wavelet bases on the DDSM dataset

From: Automatic detection of anomalies in screening mammograms

Wavelet basis§ Best feature combination Sensitivity*(%) Specificity(%) Classification rate(%)
Haar M-h1 M-d1 S-h3 99.2 36.6 60.3
Db 2 M-h3 M-d8 S-h5 97.4 42.7 63.4
Db 4 M-h8 M-d1 S-h5 95.2 20.8 49
Db 8 M-h6 S-v8 S-d3 97.5 40.4 62
Bior 1.5 M-d4 S-h6 --- 96.9 38.8 60.8
Bior 2.2 M-h5 M-v2 S-d2 98.8 44.8 65.2
Bior 2.8 M-d4 S-d2 S-a5 92.9 46.9 64.4
Bior 3.7 M-d4 S-h4 S-d4 98.9 28.1 54.9
Bior 4.4 M-h1 M-d4 S-d2 96.1 43 63.1
Bior 5.5 M-h6 M-d5 S-d2 98.5 38.1 61
Bior 6.8 M-v3 M-d4 S-d2 98 39 61.3
  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 decomposition 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.