## 4 Results
### Evaluation
#### 4.1.2 Comparative Evaluation  
Further, we evaluated the comparative performance of DCGAN augmentation method. Improved data augmentation reduces over-fitting and results in better test accuracy. Thus, we trained a neural network classifier using GAN augmented dataset along with traditional augmented datasets with the architecture shown in Figure 7. The network takes an image of size 224 \(\times\) 224 \(\times\) 3 as an input, which is followed by two convolution layers to output a representation of size 224 \(\times\) 224 \(\times\) 16 and is subjected to the MaxPooling layer to output a representation of size 112 \(\times\) 112 \(\times\) 3. The output from the MaxPooling layer is subjected to four Convolutional blocks, where each convolution block consists of two separable convolution layers with ReLU activation, batch normalization, maxpooling, and dropout to output a representation of size 7 \(\times\) 7 \(\times\) 256. The 7 \(\times\) 7 \(\times\) 256 representation is flattened and then passed on to a series of dense blocks, which consists of a dense layer with batch normalization and dropout. The output from the last dense block is passed on to the softmax layer to output the probability of an image as Normal or Pneumonia. We used accuracy and receiver operating characteristics as evaluation metrics for model comparison. Table 1 provides evidence that GAN based data augmentation results in improved accuracy of the classifier, which is also supported by the metrics of the confusion matrix, as shown in Table 2. While traditional data augmentation methods result in accuracy above 92%, GAN augmentation results in an accuracy of 95.5%. Contrast and brightness adjustment methods averaged 92% accuracy with varying levels of precision and recalls. Random crop and random flip methods have relatively better performance compared to adjustment methods, but still are inferior to GAN-based data augmentation. Table 1 presents the area under the ROC curve (AUC) for all data augmentation methods. The table shows that the GAN-based methods resulted in AUC of 93.6%, which is the best across all methods. While random flip method resulted in an AUC of 93.4%, all other methods have less than 93% AUC. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model/ Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** & **FPR** & **TPR** \\ \hline
**Randomflip-leftright** & 94.9 & 96.39 & 96.6 & 96.5 & 93.4 & 0.09 & 0.97 \\
**RandomCrop** & 94.71 & 95.31 & 97.54 & 96.42 & 92.31 & 0.13 & 0.98 \\
**ClipByValue** & 94.11 & 95.17 & 96.84 & 96 & 91.8 & 0.13 & 0.97 \\
**AdjustBrightness** & 92.91 & 94.37 & 96.02 & 95.18 & 90.28 & 0.16 & 0.96 \\
**AdjustContrast** & 92.58 & 97.41 & 92.29 & 94.77 & 92.83 & 0.07 & 0.92 \\ \hline
**GAN** & 95.5 & 96.2 & 97.7 & 97 & 93.6 & 0.1 & 0.98 \\ \hline \end{tabular}
\end{table} 
Table 1: Classification performance metrics. \begin{table}
\begin{tabular}{c c c c} \hline
**Model/ Method** & **True Negative** & **False Positive** & **False Negative** & **True Positive** \\ \hline
**Randomflip-leftright** & 286 & 31 & 29 & 826 \\
**RandomCrop** & 276 & 41 & 21 & 834 \\
**ClipByValue** & 275 & 42 & 27 & 828 \\
**AdjustBrightness** & 268 & 49 & 34 & 821 \\
**AdjustContrast** & 296 & 21 & 66 & 789 \\ \hline
**GAN** & 284 & 33 & 20 & 835 \\ \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix. Figure 7: Convolutional Neural Network Architecture. Figure 8 illustrates the ROC graphically. The graph confirms the superior performance of GAN based data augmentation over traditional augmentation methods. The comparative evaluation clearly demonstrates GAN methods superiority on both accuracy and ROC metrics. Prior GAN based medical image augmentation research focused on evaluating image quality stand alone. Some works that evaluated the augmentation using ancillary tasks such as classification reported accuracy, precision, and recall in the range of 90%. Table 3 shows the details. While our task is different, the classification performance is better than these results. [PERSON] et al.