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Table 1 Performance comparison of clustering methods for breast cancer detection

From: Fuzzy technique for microcalcifications clustering in digital mammograms

Author Clustering method N. of mammograms (clusters) Sensitivity Accuracy FP/Im Precision
Nishikawa [[12]] Spatial clustering 78 (41) 85% - - -
Cihan [[16]] Subtractive clustering 34 (72) 93% - - -
Riyahi [[17]] Wavelet transform and Fuzzy clustering 47 (47) 87% - 0.5% -
Cordella [[18]] Graph-theoretical cluster analysis 40 (102) 94% - - 100%
Wang [[19]] EFCM 180 - 99.7% - -
Quintanilla [[20]] PFCM and ANN - 98.2% 99.5% - -
Malar [[21]] Wavelet and ELM 55 - 94%   -
Cheng [[22]] FLSS 40 (105) - > 97% 14% -
Our method (Private database) FCM-WF 39 (39) 93% 95% 4% 62%
Our method (MIAS database) FCM-WF 20(25) 82% 94% 4% 65%