Research Article
Automatic Detection of Microaneurysms in OCT Images Using Bag of Features
Table 1
Evaluation of the proposed method.
| Classification method | Evaluation criteria | Accuracy | Sensitivity | Specificity | Precision |
| 1 | BOF+MLP | 96.33% | 97.33% | 95.4% | 95.28% | 2 | BOF+Gaussian SVM | 91.63% | 88.27% | 95.02% | 94.65% | 3 | BOF+linear SVM | 91.13% | 86.52% | 95.78% | 95.40% | 4 | BOF+KNN | 84.19% | 73.14% | 95.28% | 93.95% | 5 | BOF+Naïve Bayes | 87.75% | 85.65% | 89.91% | 89.48% | 6 | PCA+MLP | 92.52% | 93.93% | 92.22% | 92.36% | 7 | PCA+Gaussian SVM | 83.38% | 85.53% | 81.28% | 82.16% | 8 | PCA+linear SVM | 80.94% | 77.77% | 84.15% | 83.19% | 9 | PCA+KNN | 57.32% | 14.77% | 99.39% | 96.32% | 10 | PCA+Naïve Bayes | 76.13% | 76.14% | 76.15% | 76.14% |
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