Table 3.
Summary of convolutional neural network–based methods for breast mass detection.
| Author | Method | Dataset/number | Task | Performance metric/s (value/s) | Code availability |
| Dhungel et al [78] | Hybrid CNNa+level set | Public, INbreast dataset/410 images (multiview) | Mass detection, classification of benign, and malignant | Accuracy (0.9) and sensitivity (0.98) | —b |
| Dhungel et al [79] | CRFc+CNN | Public, INbreast and DDSMd/116 and 158 images (multiview) | Lesion detection and segmentation | Dice score (0.89) | — |
| Zhu et al [80] | Fully convolutional network+ CRF | Public, INbreast and DDSM/116 and 158 images (multiview) | Lesion segmentation | Dice score (0.97) | [92] |
| Wang et al [81] | Stacked autoencoder (transfer learning) | Private, Sun Yat-Sen University/1000 Digital mammogram | Detection and classification of calcifications and masses | Accuracy (0.87) | — |
| Riddli et al [84] | Faster R-CNN (transfer learning) | Public, DDSM (2620), INbreast (115), and private dataset by Semmelweis University Budapest/847 images | Detection and classification | AUCe (0.95) | Semmelweis dataset: [93]; Code: [94] |
| Singh et al [85] | Conditional generative adversarial network and CNN | Public and private, DDSM and Reus Hospital Spain dataset/567+194 images | Lesion segmentation and shape classification | Dice score (0.94) and Jaccard Index (0.89) | — |
| Agarwal and Carson [86] | CNN (scratch based) | Public, DDSM/8750 images (multiview) | Classification of mass and calcifications | Accuracy (0.90) | — |
| Gao et al [87] | Shallow-deep convolutional neural network, ie, 4 layers CNN+ResNet | Private, Mayo Clinic Arizona (49 subjects) and public, INbreast dataset (89 subjects) (multiview) | Lesion detection and classification | Accuracy (0.9) and AUC (0.92) | — |
| Hagos et al [88] | Multi-input CNN | Private (General Electric, Hologic, Siemens) dataset/28,294 images/(multiview) | Lesion detection and classification | AUC (0.93) and CPM (0.733) | — |
| Tuwen et al [89] | Fast R-CNN and Mask R-CNN with ResNet variants as backbone | Private (General Electric, Hologic, Siemens) dataset/23,405 images (multiview) | Lesion detection and classification | Sensitivity (0.97) with 3.56 FPf per image | — |
| Jung et al [90] | RetinaNet model | Public and private, INbreast and GURO dataset by Korea University Guro Hospital/410+222 images (multiview) | Mass detection and classification | Accuracy (0.98) with 1.3 FP per image | [95] |
| Shen et al [91] | CNN end-to-end (transfer learning through visual geometry group 16 and ResNet) | Public, DDSM and INbreast/2584 +410 (multiview) | Classification of masses | AUC (0.96) | [96] |
aCNN: convolutional neural network.
bNot available.
cCRF: conditional random field.
dDDSM: Digital Database for Screening Mammography.
eAUC: area under the curve.
fFP: false positive.