## 4 Experimental Results and Discussion
### Performance Evaluation of the Deep Learning Models
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Network** & **Fold** & **Sensitivity** & **Specificity** & **Accuracy** & **B. Accuracy** & **F1 Score** \\ \hline ResNet50 & 1 & 0.9315 & 0.9067 & 0.9189 & 0.9191 & 0.9189 \\ ResNet50 & 2 & 0.9589 & 0.9867 & 0.9730 & 0.9728 & 0.9722 \\
**ResNet50** & **3** & **0.9863** & **0.9600** & **0.9730** & **0.9732** & **0.9730** \\ ResNet50 & 4 & 0.9178 & 0.9467 & 0.9324 & 0.9322 & 0.9306 \\ ResNet50 & 5 & 0.8904 & 0.9200 & 0.9054 & 0.9052 & 0.9028 \\ ResNet50 & M \(\pm\) SD & 0.9370 \(\pm\) 0.0370 & 0.9440 \(\pm\) 0.0318 & 0.9405 \(\pm\) 0.0311 & 0.9405 \(\pm\) 0.0311 & 0.9395 \(\pm\) 0.0318 \\ \hline InceptionV3 & 1 & 0.9589 & 0.8800 & 0.9189 & 0.9195 & 0.9211 \\ InceptionV3 & 2 & 0.9452 & 0.8667 & 0.9054 & 0.9059 & 0.9079 \\
**InceptionV3** & **3** & **0.9726** & **0.9067** & **0.9392** & **0.9396** & **0.9404** \\ InceptionV3 & 4 & 0.9178 & 0.9333 & 0.9257 & 0.9256 & 0.9241 \\ InceptionV3 & 5 & 0.9178 & 0.9200 & 0.9189 & 0.9189 & 0.9178 \\ InceptionV3 & M \(\pm\) SD & 0.9425 \(\pm\) 0.0245 & 0.9013 \(\pm\) 0.0276 & 0.9216 \(\pm\) 0.0123 & 0.9219 \(\pm\) 0.0122 & 0.9223 \(\pm\) 0.0118 \\ \hline
**Xception** & **1** & **0.9452** & **0.9067** & **0.9257** & **0.9259** & **0.9262** \\ Xception & 2 & 0.9315 & 0.8933 & 0.9122 & 0.9124 & 0.9128 \\ Xception & 3 & 0.9452 & 0.8267 & 0.8851 & 0.8859 & 0.8903 \\ Xception & 4 & 0.9178 & 0.9067 & 0.9122 & 0.9122 & 0.9116 \\ Xception & 5 & 0.9315 & 0.8133 & 0.8716 & 0.8724 & 0.8774 \\ Xception & M \(\pm\) SD & 0.9342 \(\pm\) 0.0115 & 0.8693 \(\pm\) 0.0456 & 0.9014 \(\pm\) 0.0222 & 0.9018 \(\pm\) 0.0219 & 0.9036 \(\pm\) 0.0195 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Network** & **Fold** & **Sensitivity** & **Specificity** & **Accuracy** & **B. Accuracy** & **F1 Score** \\ \hline VGG19 & 1 & 0.8000 & 0.8769 & 0.8692 & 0.8384 & 0.5501 \\ VGG19 & 2 & 0.8167 & 0.8472 & 0.8442 & 0.8319 & 0.5117 \\ VGG19 & 3 & 0.7333 & 0.9269 & 0.9075 & 0.8301 & 0.6132 \\ VGG19 & 4 & 0.7833 & 0.8537 & 0.8467 & 0.8185 & 0.5054 \\
**VGG19** & **5** & **0.8833** & **0.8898** & **0.8892** & **0.8866** & **0.6145** \\ VGG19 & M \(\pm\) SD & 0.8033 \(\pm\) 0.0545 & 0.8789 \(\pm\) 0.0319 & 0.8713 \(\pm\) 0.0273 & 0.8411 \(\pm\) 0.0264 & 0.5590 \(\pm\) 0.0529 \\ \hline ResNet50 & 1 & 0.7250 & 0.9620 & 0.9383 & 0.8435 & 0.7016 \\
**ResNet50** & **2** & **0.8417** & **0.9009** & **0.8950** & **0.8713** & **0.6159** \\ ResNet50 & 3 & 0.8000 & 0.8981 & 0.8883 & 0.8491 & 0.5890 \\ ResNet50 & 4 & 0.7833 & 0.9296 & 0.9150 & 0.8565 & 0.6483 \\ ResNet50 & 5 & 0.8083 & 0.8065 & 0.8067 & 0.8074 & 0.4554 \\ ResNet50 & M \(\pm\) SD & 0.7917 \(\pm\) 0.0429 & 0.8994 \(\pm\) 0.0580 & 0.8887 \(\pm\) 0.0498 & 0.8456 \(\pm\) 0.0237 & 0.6020 \(\pm\) 0.0921 \\ \hline InceptionV3 & 1 & 0.7500 & 0.9435 & 0.9242 & 0.8468 & 0.6642 \\ InceptionV3 & 2 & 0.8333 & 0.9065 & 0.8992 & 0.8699 & 0.6231 \\
**InceptionV3** & **3** & **0.8500** & **0.9389** & **0.9300** & **0.8944** & **0.7083** \\ InceptionV3 & 4 & 0.6750 & 0.9843 & 0.9533 & 0.8296 & 0.7431 \\ InceptionV3 & 5 & 0.7750 & 0.9426 & 0.9258 & 0.8588 & 0.6764 \\ InceptionV3 & M \(\