Review Article
Involvement of Machine Learning for Breast Cancer Image Classification: A Survey
Table 21
Semisupervised algorithm for breast image classification.
| Reference | Descriptor | Image type | Number of images | Key finding |
| Cordeiro et al. [166] | Zernike moments have been used for the feature extraction. | — | 685 | Semisupervised Fuzzy GrowCut algorithm utilized. For the fatty-tissue classification this method achieved 91.28% Accuracy. |
| Cordeiro et al. [167] | — | Mammogram | 322 | Semisupervised Fuzzy GrowCut as well as the Fuzzy GrowCut algorithm utilized for tumors, region segmentation. |
| Nawel et al. [168] | — | — | — | Semisupervised Support Vector Machine (S3VM) utilized. This experiment shows impressive results on the DDSM database. |
| Zemmal et al. [169] | — | DDSM | — | Transductive semisupervised learning technique using (TSVM) utilized for classification along with different features. |
| Zemmal et al. [170] | — | — | 200 | Semisupervised Support Vector Machine (S3VM) utilized with various kernels. |
| Zemmal et al. [171] | GLCM Hu moments Central Moments | Mammogram | — | Transductive Semisupervised learning technique used for image classification. This experiment shows impressive results on DDSM database. |
| Peikari et al. [172] | Mean, Mode, Standard Deviation, Media, Skewness, Kurtosis | Histopathological | 322 | The Ordering Points to Identify the Clustering Structure (OPTICS) method utilized for image classification [173]. |
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