Table 3.
Classification results obtained by GLDS, GLRLM, and GLCM features with various classifiers from NS, SLT, and composite NS-SLT methods, respectively. The highlighted accuracy in bold indicates the best classification result.
| Features | Classifier methods | Techniques | Performance metrics | ||||
|---|---|---|---|---|---|---|---|
| Accuracy (%) | Precision | Sensitivity | Specificity | AUC | |||
| GLDS | DT-NN | NS | 85.61 ± 2.83 | 0.8 ± 0.100 | 0.68 ± 0.07 | 0.91 ± 0.03 | 0.81 ± 0.06 |
| SLT | 71.40 ± 4.20 | 0.51 ± 0.09 | 0.48 ± 0.18 | 0.81 ± 0.06 | 0.70 ± 0.08 | ||
| NS-SLT | 80.44 ± 5.35 | 0.67 ± 0.13 | 0.68 ± 0.16 | 0.85 ± 0.04 | 0.83 ± 0.06 | ||
| SVM-NN | NS | 83.17 ± 3.22 | 0.97 ± 0.02 | 0.37 ± 0.12 | 1.00 ± 000 | 0.85 ± 0.10 | |
| SLT | 73.76 ± 1.76 | 0.72 ± 0.02 | 0.24 ± 0.05 | 0.94 ± 0.01 | 0.81 ± 0.01 | ||
| NS-SLT | 81.18 ± 0.70 | 0.90 ± 0.03 | 0.41 ± 0.02 | 0.97 ± 0.01 | 0.85 ± 0.01 | ||
| KNN-NN | NS | 87.70 ± 3.22 | 0.77 ± 0.05 | 0.79 ± 0.12 | 0.90 ± 0.02 | 0.85 ± 0.05 | |
| SLT | 74.53 ± 2.74 | 0.55 ± 0.08 | 0.59 ± 0.09 | 0.81 ± 0.02 | 0.69 ± 0.04 | ||
| NS-SLT | 82.76 ± 2.15 | 0.74 ± 0.05 | 0.65 ± 0.05 | 0.90 ± 0.02 | 0.77 ± 0.03 | ||
| NB-NN | NS | 76.08 ± 1.52 | 0.56 ± 0.12 | 0.36 ± 0.04 | 0.90 ± 0.01 | 0.72 ± 0.02 | |
| SLT | 74.15 ± 1.42 | 0.62 ± 0.11 | 0.35 ± 0.03 | 0.90 ± 0.01 | 0.81 ± 0.01 | ||
| NS-SLT | 91.41 ± 1.74 | 0.93 ± 000 | 0.77 ± 0.04 | 0.97 ± 0.01 | 0.91 ± 0.02 | ||
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| GLRLM | DT-NN | NS | 92.29 ± 2.29 | 0.87 ± 0.10 | 0.85 ± 0.06 | 0.94 ± 0.05 | 0.90 ± 0.05 |
| SLT | 98.57 ± 0.71 | 0.97 ± 0.02 | 0.97 ± 0.01 | 0.99 ± 0.00 | 0.98 ± 0.00 | ||
| NS-SLT | 98.59 ± 0.70 | 0.97 ± 0.02 | 0.97 ± 0.01 | 0.99 ± 0.00 | 0.98 ± 0.01 | ||
| SVM-NN | NS | 89.84 ± 1.36 | 0.98 ± 0.01 | 0.62 ± 0.01 | 0.99 ± 0.00 | 0.98 ± 0.00 | |
| SLT | 90.13 ± 0.80 | 0.91 ± 0.03 | 0.71 ± 0.01 | 0.96 ± 0.01 | 0.89 ± 0.03 | ||
| NS-SLT | 98.94 ± 0.02 | 0.96 ± 0.00 | 1.00 ± 0.00 | 0.98 ± 0.00 | 0.99 ± 0.00 | ||
| KNN-NN | NS | 96.49 ± 1.04 | 0.96 ± 0.03 | 0.90 ± 0.05 | 0.98 ± 0.01 | 0.94 ± 0.03 | |
| SLT | 98.22 ± 0.04 | 0.95 ± 0.02 | 0.98 ± 0.01 | 0.98 ± 0.00 | 0.98 ± 0.01 | ||
| NS-SLT | 98.23 ± 0.38 | 0.96 ± 0.01 | 0.97 ± 0.01 | 0.98 ± 0.00 | 0.98 ± 0.00 | ||
| NB-NN | NS | 83.89 ± 2.81 | 0.78 ± 0.15 | 0.62 ± 0.06 | 0.91 ± 0.02 | 0.87 ± 0.01 | |
| SLT | 90.53 ± 3.16 | 0.88 ± 0.03 | 0.75 ± 0.12 | 0.96 ± 0.01 | 0.94 ± 0.02 | ||
| NS-SLT | 98.58 ± 0.36 | 0.95 ± 0.01 | 1.00 ± 0.00 | 0.98 ± 0.00 | 0.98 ± 0.01 | ||
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| GLCM | DT-NN | NS | 94.75 ± 1.60 | 0.92 ± 0.03 | 0.88 ± 0.04 | 0.97 ± 0.01 | 0.95 ± 0.02 |
| SLT | 89.16 ± 2.09 | 0.78 ± 0.08 | 0.85 ± 0.10 | 0.90 ± 0.02 | 0.90 ± 0.05 | ||
| NS-SLT | 96.10 ± 2.11 | 0.94 ± 0.02 | 0.93 ± 0.04 | 0.97 ± 0.02 | 0.95 ± 0.01 | ||
| SVM-NN | NS | 93.37 ± 0.00 | 0.93 ± 0.00 | 0.80 ± 0.00 | 0.98 ± 0.00 | 0.98 ± 0.01 | |
| SLT | 90.53 ± 2.52 | 0.98 ± 0.01 | 0.65 ± 0.10 | 0.99 ± 0.00 | 0.97 ± 0.00 | ||
| NS-SLT | 97.63 ± 0.36 | 0.97 ± 0.01 | 0.94 ± 0.01 | 0.98 ± 0.00 | 0.95 ± 0.01 | ||
| KNN-NN | NS | 91.21 ± 3.54 | 0.86 ± 0.13 | 0.82 ± 0.08 | 0.94 ± 0.03 | 0.88 ± 0.03 | |
| SLT | 81.45 ± 1.77 | 0.78 ± 0.10 | 0.47 ± 0.07 | 0.93 ± 0.01 | 0.70 ± 0.03 | ||
| NS-SLT | 97.65 ± 0.38 | 0.96 ± 0.00 | 0.95 ± 0.01 | 0.98 ± 0.00 | 0.97 ± 0.00 | ||
| NB-NN | NS | 93.71 ± 1.39 | 0.90 ± 0.03 | 0.87 ± 0.05 | 0.96 ± 0.01 | 0.97 ± 0.01 | |
| SLT | 87.00 ± 2.48 | 0.81 ± 0.06 | 0.70 ± 0.04 | 0.92 ± 0.02 | 0.95 ± 0.00 | ||
| NS-SLT | 95.29 ± 2.73 | 0.96 ± 0.00 | 0.88 ± 0.10 | 0.98 ± 0.00 | 0.94 ± 0.11 | ||