# Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
## 4 Experiments
### Classification Results
#### 4.4.2 Classification Results of 13 CNN-Based Deep Learning Models with Standard FC/Spinal FC  
The training accuracy, test accuracy, precision, recall, F1-score, and Cohen's kappa scores obtained as the result of the classification performed with each model are presented in the following figures and Tables 7-12. \begin{table}
\begin{tabular}{l l} \hline \hline
**Confusion Matrix** & TP, TN, FP, FN \\ \hline
**Training/Testing Accuracy** & (TP + TN)/(TP + TN + FP + FN) \\
**Precision** & TP/(TP + FP) \\
**Recall** & TP/(TP + FN) \\
**F1-score** & 2 TP/(2 TP + FP + FN) \\
**[PERSON]’s kappa score** & (p\({}_{0}\) – p\({}_{e}\))/(1 – p\({}_{e}\)) \\
**ROC curve** & TP rate—FP rate change \\
**AUC scores** & Area under the ROC curve \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation metrics. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Label Value** & **Prediction Value** \\ \hline
**TP** & positive & positive \\
**TN** & negative & negative \\
**FP** & positive & negative \\
**FN** & negative & positive \\ \hline \hline \end{tabular}
\end{table} 
Table 3: TP, TN, FP, FN and FN calculations. \begin{table}
\begin{tabular}{l l} \hline \hline
**P\({}_{0}\)** & (TP + TN)/(TP + TN + FP + FN) \\ \hline
**P\({}_{\text{positive}}\)** & (TP + FP)(TP + FN)/(TP + TN + FP + FN)\({}^{2}\) \\
**P\({}_{\text{negative}}\)** & (FN + TN)(FP + TN)/(TP + TN + FP + FN)\({}^{2}\) \\
**P\({}_{\text{e}}\)** & P\({}_{\text{positive}}\) + P\({}_{\text{negative}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix. \begin{table}
\begin{tabular}{l l} \hline \hline
**P\({}_{0}\)** & (TP + TN)/(TP + TN + FP + FN) \\ \hline
**P\({}_{\text{positive}}\)** & (TP + FP)(TP + FN)/(TP + TN + FP + FN)\({}^{2}\) \\
**P\({}_{\text{negative}}\)** & (FN + TN)(FP + TN)/(TP + TN + FP + FN)\({}^{2}\) \\
**P\({}_{\text{e}}\)** & P\({}_{\text{positive}}\) + P\({}_{\text{negative}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Calculation of [PERSON]’s kappa score parameters. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Models**} & **Standard** & **Spinal** & **Models** & **Standard** & **Spinal** \\  & **FC** & **Net** & **FC** & **Net** \\ \hline DenseNet169 & 0.8883 & 0.8666 & ResNet101 & 0.8419 & 0.8225 \\ DenseNet201 & 0.8934 & 0.8694 & ResNet152 & 0.845 & 0.8346 \\ InceptionV3 & 0.8882 & 0.8914 & ResNeXt50 & 0.8707 & 0.8654 \\ MobileNetV2 & 0.8647 & 0.8845 & ResNeXt101 & 0.8539 & 0.8303 \\ ResNet34 & 0.8673 & 0.8517 & VGG13 & **0.9389** & 0.8507 \\ ResNet50 & 0.8489 & 0.8395 & VGG16 & 0.8698 & 0.812 \\  & & VGG19 & 0.9055 & 0.8011 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Training accuracy results of classification models. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Models**} & **Standard** & **Spinal** & **Models** & **Standard** & **Spinal** \\  & **FC** & **Net** & **FC** & **Net** \\ \hline DenseNet169 & **0.845** & 0.815 & ResNet101 & 0.815 & 0.82 \\ DenseNet201 & 0.82 & 0.83 & ResNet152 & 0.815 & 0.82 \\ InceptionV3 & 0.825 & 0.82 & ResNeXt50 & 0.82 & 0.825 \\ MobileNetV2 & 0.83 & 0.81 & ResNeXt101 & 0.82 & 0.815 \\ ResNet34 & 0.825 & 0.82 & VGG13 & 0.8 & 0.825 \\ ResNet50 & 0.815 & 0.81 & VGG16 & 0.79 & 0.805 \\  & & & VGG19 & 0.785 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Recall results of classification models. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Models**} & **Standard** & **Spinal** & **Models** & **Standard** & **Spinal** \\  & **FC** & **Net** & **FC** & **Net** \\ \hline DenseNet169 & **0.84** & 0.815 & ResNet101 & 0.815 & 0.82 \\ DenseNet201 & 0.82 & 0.83 & ResNet152 & 0.815 & 0.82 \\ InceptionV3 & 0.825 & 0.82 & ResNeXt50 & 0.82 & 0.825 \\ MobileNetV2 & 0.83 & 0.81 & ResNeXt101 & 0.82 & 0.815 \\ ResNet34 & 0.825 & 0.82 & VGG13 & 0.8 & 0.825 \\ ResNet50 & 0.815 & 0.81 & VGG16 & 0.79 & 0.805 \\  & & & VGG19 & 0.785 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Precision results of classification models.