Table 5.
Comparison with other works in the literature on osteoporosis classification
| Work carried | Approach | Performance |
|---|---|---|
| [7] | RB-SVM + hip radiographs | 90% Acc, 90% Sn, 87% Sp |
| [8] | HGSF classifier + DPRs of lumbar spine | 96.01% Acc, 95.3% Sn, 94.7% Sp |
| HGSF classifier + DPRs of femoral neck | 98.9% Acc, 99.1% Sn, 98.4% Sp | |
| [9] | regression SVM + factors from dietary and lifestyle habits | values not mentioned |
| [10] | RB-SVM + kNN + micro-CT images | values not mentioned |
| [11] | HAC algorithm + RB- SVM + DPRs of lumbar spine | 93% Acc, 95.8% Sn, 86.6% Sp |
| HAC algorithm + RB-SVM + DPRs of femoral neck | 89% Acc, 96% Sn, 84% Sp | |
| [12] | SVM + X-ray images | 95% Acc, Sn and Sp not mentioned |
| [13] | MFFN + WFS | Acc not mentioned, 57.9% Sn, 68.9% Sp |
| Naïve Bayes + WFS | Acc not mentioned, 0% Sn, 62% Sp | |
| LR + WFS | Acc not mentioned, 40.7% Sn, 62.3% Sp | |
| our proposed method | MBO-ANN + DPRs of lumbar spine | 97.9% Acc, 95.2% Sn, 98.3% Sp |
| MBO-ANN + DPRs of femoral neck | 99.3% Acc, 100% Sn, 99.2% Sp |