|
| Ref. | Classifier method | Dataset | Features | Metrics |
|
| Jaleel et al. [37] | ANN | MiniMIAS | Discrete wavelet transform and GLCM | Accuracy, sensitivity, specificity |
| Kaur et al. [38] | SVM, KNN, LDA and DT | MiniMIAS | SURF | Accuracy |
| Kamil et al. [39] | KNN | MiniMIAS | Gray level Cooccurrence matrix | Accuracy, sensitivity, specificity |
| Al-Azzam and Shatnawi [40] | Compare supervised learning (SL) with semisupervised learning (SSL) for 9 algorithms | Wisconsin diagnostic breast cancer (WDBC) dataset | 30 attributes of 569 patients with 569 instances | Accuracy, sensitivity, specificity |
| Huang et al. [41] | SVM with three functions and two features: bagging and boosting | Two datasets | First dataset: 11 attributes with 699 instances
Second dataset: 117 attributes with 102294 instances | Accuracy, sensitivity, specificity |
| Asri et al. [42] | SVM, C4.5, NB, k-NN | Wisconsin breast cancer (original) dataset (WBCD) | 11 attributes 699 instances | Accuracy, specificity |
| Rawal [43] | Compare (K-means, EM, PAM, and fuzzy C-means) with SVM and C5.0 | Wisconsin prognostic breast cancer dataset | 32 attributes with 194 instances | Accuracy |
| Kourdifi and Bahaj [44] | Random forest, naive Bayes, SVM, KNN | Wisconsin breast cancer dataset | 30 attributes with 699 instances | Accuracy, sensitivity, specificity |
| Cai et al. [45] | Conventional neural network (CNN) | The datasets were collected at two medical institutions | 990 images, 540 malignant masses, and 450 benign lesions | Accuracy |
| Ionescu et al. [46] | CNN | Private dataset | 67,520 mammographic images from 16,968 women | Accuracy, sensitivity, specificity |
| Zebari et al. [47] | Five classifiers with ANN | Mini-MIAS, and other datasets | (i) LBP
(ii) FD
(iii) Proposed M-FD | Accuracy, sensitivity, specificity |
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