Table 13.

Logic Based.

Reference	Descriptor	Image type	Number of images	Key findings
Angayarkanni and Kamal [102]	(1) GLCM	Mammogram	322	(1) The Achieved Sensitivity and Accuracy are 93.40% and 99.50%, respectively.
Wang et al. [103]	(1) Horizontal Weighted Sum  (2) Vertical Weighted Sum  (3) Diagonal Weighted Sum  (4) Grid Weighted Sum.	Mammogram	322	(1) Surrounding Region Dependence Method (SRDM) utilized for region detection. (2) Achieved True Positive Rate 90.00% and False Positive Rate 88.80%.
Tambasco Bruno et al. [104]	(1) Curvelet Transform  (2) LBP	Mammogram  Histopathological	—	(1) ANOVA method utilized for feature prioritization. (2) When they use RF algorithm on Mammogram (DDSM) dataset, obtained Accuracy and ROC are 79.00% and 0.89.
Muramatsu et al. [105]	(1) Radial Local Ternary Pattern (RLTP)	Mammogram	376	(1) Textural features have been extracted from the regions of interest (ROIs) using RLTP. (2) They claimed that the RLTP feature provides better performance than the rotation invariant patterns.
Dong et al. [106]	(1) NRL margin gradient  (2) Gray-level histogram  (3) Pixel value fluctuation	Mammogram	—	(1) Chain code utilized for extraction of regions of interest (ROIs). (2) Rough-Set method utilized to enhance the ROIs. (3) Their achieved ROC value is 0.947 and obtained Matthews Correlation (MCC) is 0.8652.
Piantadosi et al. [107]	(1) Local Binary Pattern-Three Orthogonal Projections (LBP-TOP)	Mammogram	—	(1) Their achieved Accuracy, Sensitivity, and Specificity values are 84.60%, 80.00%, and 90.90%.