|
| Method | Extracted features | Utilized classifiers | Purpose |
|
| [6] | Local binary pattern (LBP) | Support vector machines (SVMs) | Classification of ROIs into mass/normal |
| [7] | Histogram of oriented gradients (HOG) | SVM | Classification of ROIs into mass/normal |
| [11] | Haralick’s features (HAR) | -nearest neighbour (-NN) | Microcalcification classification |
| [8] | Gabor filters (GF) | Threshold-based approach | Breast cancer detection |
| [29] | Grey levels, texture, and features related to independent component analysis | Neural network (NN) | Classifying ROIs into normal/abnormal
Classifying ROIs into benign/malignant |
| [30] | A set of texture features | SVM | Mass detection |
| [31] | Ripley’s function texture measures | SVM | Detection of breast masses |
| [9] | Texture features derived from concurrence matrix | NN | Microcalcification classification |
| [32] | A set of texture features | -NN, SVM, random forests, logistic model trees, and Naive Bayes | Lesion classification |
| [10] | HAR | Bayesian classifier
Fisher linear discriminant | Study the effect of pixel resolution on the performance of texture methods |
| [3] | LBP, robust LBP, centre symmetric LBP, fuzzy LBP, local grey level appearance, LDN, HOG, HAR, and GF | -NN, linear SVM, nonlinear SVM random forest, and Fisher linear discriminant analysis (FLDA) | Finding the best combination among the texture methods to classify ROIs into mass/normal |
| [33] | Local ternary pattern and local phase quantization | SVM | Classifying tumors into benign/malignant |
| [34] | Novel sets of texture descriptors extracted from the cooccurrence matrix | SVM | Six medical datasets were used for validation, one of them for breast cancer |
| [35] | Texture analysis techniques based on the cooccurrence matrix and region-based approaches | SVM | 15 datasets were used for validation, one of them for breast cancer |
| [36] | HOG, dense scale invariant feature transform, and local configuration pattern | SVM, -NN, FLDA, and decision tree | Classifying ROIs into normal/abnormal
Classifying ROIs into benign/malignant |
| [37] | Curvelet moments | -NN | Classifying ROIs into normal/abnormal
Classifying ROIs into benign/malignant |
|
