# 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
\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 & 0.81 & 0.815 \\ InceptionV3 & 0.825 & 0.815 & ResNeXt50 & 0.815 & 0.825 \\ MobileNetV2 & 0.82 & 0.81 & ResNeXt101 & 0.82 & 0.815 \\ ResNet34 & 0.82 & 0.82 & VGG13 & 0.775 & 0.82 \\ ResNet50 & 0.815 & 0.805 & VGG16 & 0.785 & 0.8 \\  & & & VGG19 & 0.785 & 0.805 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: F1-score results of classification models. Figure 21 shows that the highest training accuracy in classification is achieved by VGG13 among models with Standard FC and by InceptionV3 among models with Spinal FC. The test accuracy in Figure 22, the precision in Figure 23, the recall in Figure 24, the F1-score in Figure 25, and [PERSON]'s kappa score in Figure 26 show that the highest scores are achieved by DenseNet169 among models with Standard FC and by DenseNet201 among models with Spinal FC. Figure 21: Training accuracy results of classification models. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Models**} & **Standard** & **Spinal** & \multirow{2}{*}{**Models**} & **Standard** & **Spinal** \\  & **FC** & **Net** & & **FC** & **Net** \\ \hline DenseNet169 & **0.6834** & 0.6302 & ResNet101 & 0.634 & 0.6372 \\ DenseNet201 & 0.641 & 0.6588 & ResNet152 & 0.6231 & 0.6332 \\ InceptionV3 & 0.6514 & 0.6338 & ResNeXt50 & 0.6338 & 0.648 \\ MobileNetV2 & 0.6478 & 0.6195 & ResNeXt101 & 0.641 & 0.6267 \\ ResNet34 & 0.6372 & 0.6411 & VGG13 & 0.558 & 0.6442 \\ ResNet50 & 0.6375 & 0.6161 & VGG16 & 0.5695 & 0.6014 \\  & & & VGG19 & 0.5698 & 0.6085 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: [PERSON]’s kappa results of classification models. Figure 22: Test accuracy results of classification models. Figure 23: Precision results of classification models. Figure 24: Recall results of classification models. Figure 25: F1-score results of classification models. The confusion matrix values and AUC scores achieved as a result of 26 classification procedures, performed with DenseNet, Inception, MobileNet, ResNet, ResNeXt, and VGG models with Standard FC and Spinal FC with different numbers of layers, are provided in Table 13. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Models** & **TP** & **FP** & **FN** & **TN** & **Class0: AUC** & **Class1: AUC** \\ \hline DenseNet169a & 222 & 33 & 56 & 252 & **0.8809** & **0.8797** \\ DenseNet169b & 218 & 44 & 60 & 241 & 0.8602 & 0.8598 \\ DenseNet201a & 225 & 48 & 53 & 237 & 0.8584 & 0.8653 \\ DenseNet201b & 228 & 46 & 50 & 239 & 0.8727 & 0.8724 \\ InceptionV3a & 218 & 38 & 60 & 247 & 0.8754 & 0.8707 \\ InceptionV3b & 220 & 45 & 58 & 240 & 0.8582 & 0.8585 \\ MobileNetV2a & 215 & 36 & 63 & 249 & 0.8777 & 0.8378 \\ MobileNetV2b & 216 & 45 & 62 & 240 & 0.8633 & 0.861 \\ ResNet34a & 215 & 39 & 63 & 246 & 0.8705 & 0.8767 \\ ResNet34b & 228 & 51 & 50 & 234 & 0.8617 & 0.8619 \\ ResNet50a & 224 & 48 & 54 & 237 & 0.8715 & 0.8662 \\ ResNet50b & 219 & 49 & 59 & 236 & 0.8588 & 0.8584 \\ ResNet101a & 228 & 53 & 50 & 232 & 0.8683 & 0.8703 \\ ResNet101b & 216 & 40 & 62 & 245 & 0.8609 & 0.861 \\ ResNet152a & 217 & 45 & 61 & 240 & 0.8648 & 0.8701 \\ ResNet152b & 220 & 45 & 58 & 240 & 0.8597 & 0.8606 \\ ResNeX50a & 221 & 46 & 57 & 239 & 0.8644 & 0.8699 \\ ResNeXt50b & 223 & 44 & 55 & 241 & 0.8789 & 0.8783 \\ ResNeXt101a & 225 & 48 & 53 & 237 & 0.8772 & 0.8765 \\ ResNeXt101b & 219 & 46 & 59 & 239 & 0.8652 & 0.8561 \\ VGG13a & 181 & 27 & 97 & **258** & 0.8406 & 0.8415 \\ VGG13b & **231** & 35 & 65 & 250 & 0.8705 & 0.8737 \\ VGG16a & 203 & 46 & 75 & 239 & 0.8517 & 0.8523 \\ VGG16b & 204 & 38 & 74 & 247 & 0.8542 & 0.857 \\ VGG19a & 212 & 55 & 66 & 230 & 0.8374 & 0.8502 \\ VGG19b & 205 & 37 & 73 & 248 & 0.858 & 0.8539 \\ \hline \hline \end{tabular}
\end{table}