Table 21.

Semisupervised algorithm for breast image classification.

Reference	Descriptor	Image type	Number of images	Key finding
Cordeiro et al. [166]	(1) Zernike moments have been used for the feature extraction.	—	685	(1) Semisupervised Fuzzy GrowCut algorithm utilized. (2) For the fatty-tissue classification this method achieved 91.28% Accuracy.
Cordeiro et al. [167]	—	Mammogram	322	(1) Semisupervised Fuzzy GrowCut as well as the Fuzzy GrowCut algorithm utilized for tumors, region segmentation.
Nawel et al. [168]	—	—	—	(1) Semisupervised Support Vector Machine (S3VM) utilized. (2) This experiment shows impressive results on the DDSM database.
Zemmal et al. [169]	—	DDSM	—	(1) Transductive semisupervised learning technique using (TSVM) utilized for classification along with different features.
Zemmal et al. [170]	—	—	200	(1) Semisupervised Support Vector Machine (S3VM) utilized with various kernels.
Zemmal et al. [171]	(1) GLCM (2) Hu moments (3) Central Moments	Mammogram	—	(1) Transductive Semisupervised learning technique used for image classification. (2) This experiment shows impressive results on DDSM database.
Peikari et al. [172]	(1) Mean, Mode, Standard Deviation, Media, Skewness, Kurtosis	Histopathological	322	(1) The Ordering Points to Identify the Clustering Structure (OPTICS) method utilized for image classification [173].