# Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis
## 4 Experimental Results
### Discussion of Experiments  
In the experiments of image segmentation, based on the modeling of U-Net and U-Net++, the results of the experiments were taken to the last two decimal places, in which the results of the femoral neck, Ward's triangle, and total hip were the same, and the results of U-Net++ in the region of the greater trochanter were a little bit higher than the results of U-Net, and the results of all segmentation results were greater than equal to 0.5, so all of the segmentations can be considered effective image segmentation. In the experiments on image classification, we divided them into three experiments. The first experiment was to use the classification results of the original dataset, whether using T-score or BMD as the index for osteoporosis classification. Most of the experimental results had the problem of underfitting as well as the problem of data imbalance or model overfitting in the greater trochanter and Ward's triangle, so it is necessary to improve the accuracy and generalization ability of the model through the methods of data augmentation and data balancing. The second experiment is the classification of results using data augmentation. Whether using T-score or BMD as the index for osteoporosis classification, on the whole, most of the experimental results have improved after using data augmentation. However, there are still lower values of sensitivity and specificity in the big rumble and Ward's triangle, probably because the big rumble and Ward's triangle are poorly balanced in the original dataset. Thus, even with data augmentation and balancing, it still cannot make up for the lower values of sensitivity and specificity due to the imbalance of the data. However, compared to the situation before using the data augmentation, it has improved quite a lot. The third experiment was to use the results of image segmentation. We then added the best results of the two osteoporosis classification indexes to the image segmentation method to test whether it could improve the classification ability of the model. However, in the classification results after adding the image segmentation, the overall accuracy of the hip and the F1-score did not improve, and the accuracy dropped \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Using T-Score as an Indicator**} \\ \hline \hline \multicolumn{10}{c}{**Total Hip**} \\ \hline \multicolumn{10}{c}{**Indicators**} & \multicolumn{1}{c}{**Sensitivity**} & \multicolumn{1}{c}{**Specificity**} & \multicolumn{1}{c}{**F1-score**} & \multicolumn{1}{c}{**Accuracy**} \\ \hline  & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** \\ \hline DenseNet121 & 0.98 & 0.79 & 0.97 & 0.44 & 0.97 & 0.65 & 0.97 & 0.60 \\ \hline \multicolumn{10}{c}{Using BMD as an indicator} \\ \hline VGG16 & 0.99 & 0.70 & 0.96 & 0.56 & 0.98 & 0.59 & 0.98 & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Experimental results of image categorization of total hip. from the highest of 74% to 60%, regardless of whether the osteoporosis classification indexes were based on the T-score or the BMD. We hypothesize that the reason may be that the X-ray images of each part of the hip need the surrounding feature information, and the use of image cutting cuts out the surrounding feature information, which leads to the poor performance of the model classification results, so the model without cutting has a higher classification ability. Looking at the osteoporosis classification index, the experimental results of BMD and T-score do not differ too much, and the overall classification accuracy of the hip is higher than that of other parts of the body, which is more in line with the doctor's expectation, while the classification accuracy of the Ward's triangle is lower because of the serious data imbalance problem in the original dataset. Although the sensitivity and specificity of the classification results improved after data expansion, more results were still below 0.50.