# Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis
## 4 Experimental Results
### Image Classification Results
#### 4.2.2 Categorization Results Using Data Augmentation  
Using the T-score as an indicator, Table 8 shows the experimental results of image categorization for each part. The pre-trained model using DenseNet121 has the best performance on the classification test results of the total hip, with an accuracy of 0.74 and an F1-score of 0.71, while the pre-trained model using VGG16 has the worst performance on the classification test results of the Ward's triangle, with an accuracy of only 0.47. Overall, most of the experimental results are better than the classification results of the original dataset, and the problem of model under-simulation has been solved. Although the sensitivity of the big rumble and the sensitivity of the Ward's triangle are still low, the lowest value is 0.40, which is much better than the classification results of the original dataset. Using BMD as an indicator, Table 9 shows the experimental results of image categorization for each part. The pre-trained model using VGG16 performed the best in the total hip classification test with an accuracy of 0.74 and an F1-score of 0.69, while the pre-trained model using DenseNet121 performed the worst in the femoral neck classification test with an accuracy of only 0.55. Overall, most of the experimental results were better than the original dataset, which also solved the problem of model under-simulation. Overall, most of the experimental results were better than the classification results of the original dataset, and the problem of model under-simulation was solved. Although the sensitivity value of the [PERSON]'s triangle is still low (the lowest is 0.49), it is still much better than the classification result of the original dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Femoral Neck**} \\ \hline
**Indicators** & \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{2}{c}{**Specificity**} & \multicolumn{2}{c}{**F1-Score**} & \multicolumn{2}{c}{**Accuracy**} \\ \hline  & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** \\ \hline VGG16 & 0.99 & 0.76 & 0.96 & 0.43 & 0.98 & 0.73 & 0.98 & 0.64 \\ \hline ResNet50 & 0.65 & 0.63 & 0.36 & 0.42 & 0.63 & 0.62 & 0.55 & 0.56 \\ \hline DenseNet121 & 0.67 & 0.66 & 0.36 & 0.34 & 0.63 & 0.61 & 0.57 & 0.54 \\ \hline  & \multicolumn{6}{c}{Greater Trochanter} \\ \hline VGG16 & 0.97 & 0.36 & 0.99 & 0.70 & 0.98 & 0.42 & 0.98 & 0.53 \\ \hline ResNet50 & 0.46 & 0.55 & 0.54 & 0.53 & 0.42 & 0.51 & 0.50 & 0.54 \\ \hline DenseNet121 & 0.45 & 0.43 & 0.54 & 0.52 & 0.41 & 0.38 & 0.50 & 0.47 \\ \hline  & \multicolumn{6}{c}{Ward’s Triangle} \\ \hline VGG16 & 0.99 & 0.91 & 0.65 & 0.1 & 0.92 & 0.78 & 0.88 & 0.65 \\ \hline ResNet50 & 0.61 & 0.61 & 0.46 & 0.51 & 0.60 & 0.62 & 0.55 & 0.58 \\ \hline DenseNet121 & 0.77 & 0.36 & 0.21 & 0.75 & 0.68 & 0.40 & 0.59 & 0.60 \\ \hline  & \multicolumn{6}{c}{Total Hip} \\ \hline VGG16 & 0.99 & 0.54 & 0.99 & 0.80 & 0.99 & 0.57 & 0.99 & 0.70 \\ \hline ResNet50 & 0.31 & 0.38 & 0.97 & 0.70 & 0.34 & 0.39 & 0.57 & 0.58 \\ \hline DenseNet121 & 0.60 & 0.36 & 0.87 & 0.75 & 0.66 & 0.40 & 0.76 & 0.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results of image classification by parts (original dataset with BMD). Regardless of whether T-score or BMD was used as the index for osteoporosis classification, on the whole, most of the experimental results were improved after using data augmentation, although there were still lower values for sensitivity and specificity, probably