Review Article
Involvement of Machine Learning for Breast Cancer Image Classification: A Survey
Table 18
Bayesian classifier.
| Reference | Descriptor | Image type | Number of images | Key findings |
| Benndorf et al. [136] | BI-RADS features utilized. | — | 2766 | For the training data the AUC value is 0.959 for the inclusive model, whereas AUC value is 0.910 for the descriptor model. |
| Rodríguez-López and Cruz-Barbosa [137] | Eight image feature nodes utilized. | — | — | NB model obtained 79.00% Accuracy, 80.00% Sensitivity. |
| Nugroho et al. [138] | Eight image feature nodes utilized. | Mammogram | — | Naive Bayes model along with SMO; obtained ROC value is 0.903. Bayesian Network model along with SMO; obtained Accuracy was 83.68%. |
| Rodríguez-López and Cruz-Barbosa [139] | Eight image features have been utilized. | — | 231 | Bayesian Network model obtained 82.00% Accuracy, 80.00% Sensitivity, and 83.00% Specificity when they utilized only three features. |
| Shivakumari et al. [140] | | — | 231 | Analyze the Ljubljana breast image dataset. NB algorithm along with feature ranking techniques; the best achieved Accuracy was %. |
| Rodríguez-López and Cruz-Barbosa [141] | Seven different clinical features extracted. | Mammogram | 690 | Obtained Accuracy, Sensitivity, and Specificity are 82.00%, 80.00%, and 83.00%, respectively. |
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