# 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
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{10}{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 & 0.98 & 0.74 & 0.99 & 0.61 & 0.98 & 0.67 & 0.98 & 0.67 \\ \hline ResNet50 & 0.98 & 0.61 & 0.98 & 0.81 & 0.98 & 0.66 & 0.98 & 0.72 \\ \hline DenseNet121 & 0.98 & 0.64 & 0.98 & 0.77 & 0.98 & 0.67 & 0.98 & 0.71 \\ \hline  & \multicolumn{6}{c}{Greater Trochanter} \\ \hline VGG16 & 0.99 & 0.52 & 0.99 & 0.83 & 0.99 & 0.58 & 0.99 & 0.71 \\ \hline ResNet50 & 0.99 & 0.43 & 0.99 & 0.81 & 0.99 & 0.50 & 0.99 & 0.66 \\ \hline DenseNet121 & 0.98 & 0.50 & 0.97 & 0.72 & 0.98 & 0.53 & 0.98 & 0.63 \\ \hline  & \multicolumn{6}{c}{Ward’s Triangle} \\ \hline VGG16 & 0.93 & 0.61 & 0.94 & 0.42 & 0.94 & 0.64 & 0.94 & 0.55 \\ \hline ResNet50 & 0.99 & 0.75 & 0.95 & 0.40 & 0.96 & 0.73 & 0.96 & 0.63 \\ \hline DenseNet121 & 0.97 & 0.69 & 0.98 & 0.50 & 0.97 & 0.71 & 0.97 & 0.63 \\ \hline  & \multicolumn{6}{c}{Total Hip} \\ \hline VGG16 & 0.99 & 0.67 & 0.99 & 0.81 & 0.99 & 0.70 & 0.99 & 0.74 \\ \hline ResNet50 & 0.98 & 0.62 & 0.96 & 0.75 & 0.97 & 0.65 & 0.97 & 0.69 \\ \hline DenseNet121 & 0.98 & 0.68 & 0.99 & 0.80 & 0.99 & 0.71 & 0.99 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results of image classification by parts (augmentation dataset with T-score). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{10}{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 & 0.77 & 0.99 & 0.65 & 0.99 & 0.78 & 0.99 & 0.73 \\ \hline ResNet50 & 0.98 & 0.52 & 0.98 & 0.61 & 0.98 & 0.59 & 0.98 & 0.55 \\ \hline DenseNet121 & 0.99 & 0.44 & 0.99 & 0.76 & 0.99 & 0.55 & 0.99 & 0.55 \\ \hline  & \multicolumn{6}{c}{Greater Trochanter} \\ \hline VGG16 & 0.99 & 0.64 & 0.95 & 0.74 & 0.97 & 0.67 & 0.97 & 0.69 \\ \hline ResNet50 & 0.99 & 0.63 & 0.99 & 0.71 & 0.99 & 0.65 & 0.99 & 0.67 \\ \hline DenseNet121 & 0.99 & 0.71 & 0.97 & 0.66 & 0.98 & 0.70 & 0.98 & 0.69 \\ \hline  & \multicolumn{6}{c}{Ward’s Triangle} \\ \hline VGG16 & 0.96 & 0.84 & 0.97 & 0.49 & 0.96 & 0.74 & 0.96 & 0.73 \\ \hline ResNet50 & 0.96 & 0.72 & 0.94 & 0.48 & 0.95 & 0.73 & 0.95 & 0.64 \\ \hline DenseNet121 & 0.87 & 0.47 & 0.97 & 0.80 & 0.92 & 0.53 & 0.92 & 0.67 \\ \hline  & \multicolumn{6}{c}{Total Hip} \\ \hline VGG16 & 0.99 & 0.73 & 0.96 & 0.75 & 0.98 & 0.69 & 0.98 & 0.74 \\ \hline ResNet50 & 0.99 & 0.57 & 0.98 & 0.85 & 0.98 & 0.63 & 0.98 & 0.74 \\ \hline DenseNet121 & 0.94 & 0.47 & 0.99 & 0.80 & 0.96 & 0.53 & 0.97 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Experimental results of image classification by parts (augmentation dataset with BMD). because the balance of the original dataset of greater trochanter and Ward's triangle was very poor, so even though data augmentation and balancing were carried out, it still could not compensate for the lower values of sensitivity and specificity, but they were much improved compared with those before using data augmentation. Next, the best results of the two osteoporosis classification indexes were added to the image segmentation method to test whether the overall values could be further improved.