Table 1.
State-of-the-art diagnostic schemes for the screening mammography classification.
| Authors | Year | Data sources | Technique/classifier | Classes | Number of images | Classification accuracy |
|---|---|---|---|---|---|---|
| Mazurowski et al. [3] | 2011 | DDMS | Random mutation hill climbing | 2 | 1,852 | 49%–83% |
| Lesniak et al. [4] | 2011 | Private | SVM radial Kernel | 2 | 10,397 | 66%-67% |
| Wei et al. [5] | 2011 | DDSM | SVM radial Kernel | 2 | 2,563 | 72%–74% |
| Abirami et al. [7] | 2016 | MIAS | Wavelet features | 2 | 322 | 93% |
| Tagliafico et al. [34] | 2009 | Private | Thresholding | 4 | 160 | 80%–90% |
| Subashini et al. [35] | 2010 | Private | SVM radial Kernel | 3 | 43 | 95% |
| Elter and Halmeyer [8] | 2008 | DDSM | Euclidean metric | 2 | 360 | 86% |
| Deserno et al. [13] | 2011 | IRMA | SVM Gaussian Kernel | 12 | 2796 | 80% |
| Tao et al. [6] | 2011 | Private | Local linear embedding metric | 2 | 476 | 80% |
| Curvature scale space | 415 | 75% | ||||
| Ge et al. [19] | 2006 | Private | CNN and LDA | 2 | 196 | — |
| MIAS | CNN and LDA | 216 | — | |||
| Jamieson et al. [21] | 2012 | FFDM | ADN and SVM | 2 | 739 | — |
| Ultrasound | ADN and SVM | 2393 | — | |||
| Arevalo et al. [22] | 2015 | BCDR-F03 | CNN and SVM | 2 | 736 | 79.9%–86% |
| Mert et al. [23] | 2015 | WBDC | ICA and RBFNN | 2 | 569 | 90% |
| Dheeba et al. [25] | 2015 | Private | PSOWNN | 2 | 216 | 93.6% |
| Abdel-Zaher and Eldeib [26] | 2015 | WBCD | DBN | 2 | 690 | 99.6% |
| Vani et al. [10] | 2010 | MIAS | ELM | |||
| Jasmine et al. [11] | 2009 | MIAS | Wavelet & ANN | 2 | 322 | 87% |
| Xu et al. [12] | 2008 | MLPNN | 120 | 98% | ||
| Uppal and Naseem [27] | 2016 | MIAS | Fusion of cosine transform | 3 | 322 | 96.97% |