# Classification and Predictions of Lung Diseases from Chest X-rays Using MobileNet V2
## 5 Experimental Results  
The lack of computing power had a big role in limiting the experiment. In this work, we used the \"Kaggle\" open free cloud to edit and experiment with our model. Although this cloud offers a free GPU and fifteen gigabytes of ram, it was not enough to train our model over ten epochs. However, we managed to get a good result (Check Figures 8 and 9). We started by viewing the accuracy of the model and in the first epoch we had (0.951) a training accuracy and (0.913) a validation accuracy, with a difference of 3.74% between the training and validation. In the epoch number, the fourth gap between the training and validation accuracy gets much smaller with a training accuracy of 0.953 and a validation accuracy of 0.941. The difference decreases to 1.18%. In the last epoch, and we got some overfitting signs, with an increase in the difference between the training accuracy and validation accuracy with more than 2% of a difference.  
Figure 8: Model accuracy: a comparison between the training accuracy and the validation accuracy over a ten epoch.  
The same case with the other metric, which is the loss function. In the first epoch, the difference between train loss and validation loss was about 4%. In the middle epoch, the gap starts to get bigger to pass 6% of a difference. In the last epoch, it starts to settle at 4%.  
The specificity did show some promising results averaging 97%. The sensitivity on the other side did not provide a good result as it shows an average sensitivity of 45%. However, the F1-score got 55% (68% best and 40% lowest). The detail of the 14 disease class is provided in Table 4.  
We used the ROC curve to extract more detail about the result of our model. The ROC curve illustrated in Figure 10 is a plot which compares the False Positive Rate over the True Positive Rate of a predicted image.  
For the \"Emphysema\", the AUC is (0.891), and the \"Effusion\" and \"Edema\" got 0.876 and 0.884, the \"Pneumothorax\" and \"Cardiomegaly\" also had a good AUC, with 0.880 and 0.885 and \"Mass\", \"Hernia\" and \"Atelectasis\" had a good AUC (0.826, 0.811 and 0.794). For the other diseases--\"Consolidation\", \"Fibrosis\", and \"Pleural Thickening\"--the was similar (0.790, 0.762, 0.763). The last three diseases got the lowest AUC: \"Nodule\" and AUC of 0.743; \"Pneumonia\" an AUC of 0.733; and \"Infiltration\" an AUC of (0.701).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pathology** & **Accuracy** & **Sensitivity** & **Specificity** & **F1-Score** \\ \hline Atelectasis & 0.796 & 0.507 & 0.968 & 0.683 \\ Cardiomegaly & 0.964 & 0.435 & 0.989 & 0.521 \\ Consolidation & 0.919 & 0.335 & 0.991 & 0.653 \\ Edema & 0.948 & 0.500 & 0.991 & 0.635 \\ Effusion & 0.794 & 0.405 & 0.924 & 0.510 \\ Emphysema & 0.958 & 0.635 & 0.996 & 0.478 \\ Fibrosis & 0.966 & 0.472 & 0.991 & 0.652 \\ Hernia & 0.997 & 0.600 & 0.991 & 0.400 \\ Infiltration & 0.735 & 0.328 & 0.948 & 0.560 \\ Mass & 0.880 & 0.445 & 0.972 & 0.432 \\ Nodule & 0.863 & 0.443 & 0.952 & 0.423 \\ Pleural Thickening & 0.962 & 0.460 & 0.991 & 0.607 \\ Pneumonia & 0.970 & 0.380 & 0.991 & 0.558 \\ Pneumothorax & 0.882 & 0.404 & 0.933 & 0.675 \\ Average & 0.902 & 0.453 & 0.973 & 0.556 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental result of our method.  
Figure 9: Model loss: the difference between the loss under the training phase and loss under validation loss.  
As we can see in the image with the index zero from Figure 9, the ground truth label was \"Effusion\". Knowing that the model had AUC 0.89, the result was not surprising in that it gets 89.22% as a prediction. For the second image, we can see that the model starts getting a compromise and that the true diseases in this picture was \"Mass\", and the prediction of the models of two diseases got a prediction rate higher than 97%.