# Classification and Predictions of Lung Diseases from Chest X-rays Using MobileNet V2
## 7 Conclusions  
A convolutional neural network (CNN) is developed for the diagnosis of thoracic (pulmonary) diseases in an X-ray image. The outperformance achieved is mainly due to the deep structure of CNN, which uses the mining power of different level features and resulted in a better generalization ability (Figure 11 shows some prediction result on the test-set). CNN models such as the MobileNet V2 network have superior generalization capabilities and precision compared to other networks and the results obtained demonstrate the high recognition rates of the proposed CNN and show that it gave very precise results in the diagnosis of the disease in the chest X-ray and then in classification. Our optimized MobileNet V2 architecture achieves state-of-the-art results in six out of fourteen classes compared to [PERSON] et al. [40] (who had state-of-the-art results in five out of fourteen classes on the official split). For other approaches, even higher scores are reported in the literature. However, a comparison of the different CNN methods  
Figure 11: Result of the prediction attempt from the testing set: two other samples with different index labels. Each one could have one or more diseases. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pathology** & **[PERSON] et al. [20]** & **[PERSON] et al. [40]** & **[PERSON] et al. [21]** & **[PERSON] et al. [23]** & **Our Model** \\ \hline Atelectasis & 0.700 & 0.755 & 0.733 & 0.800 & 0.794 \\ Cardiomegaly & 0.810 & 0.875 & 0.856 & 0.871 & 0.885 \\ Consolidation & 0.703 & 0.749 & 0.711 & 0.801 & 0.790 \\ Edema & 0.805 & 0.846 & 0.806 & 0.881 & 0.884 \\ Effusion & 0.759 & 0.828 & 0.806 & 0.859 & 0.876 \\ Emphysema & 0.833 & 0.895 & 0.842 & 0.870 & 0.891 \\ Fibrosis & 0.786 & 0.818 & 0.743 & 0.901 & 0.762 \\ Hernia & 0.872 & 0.937 & 0.775 & 0.773 & 0.811 \\ Infiltration & 0.661 & 0.709 & 0.673 & 0.701 & 0.711 \\ Mass & 0.693 & 0.821 & 0.777 & 0.831 & 0.826 \\ Nodule & 0.669 & 0.758 & 0.724 & 0.751 & 0.743 \\ Pleural Thickening & 0.669 & 0.761 & 0.724 & 0.791 & 0.763 \\ Pneumonia & 0.658 & 0.731 & 0.684 & 0.671 & 0.733 \\ Pneumothorax & 0.799 & 0.846 & 0.805 & 0.871 & 0.880 \\ Average & 0.745 & 0.806 & 0.761 & 0.812 & 0.810 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AUC result overview for our experiment. with respect to their performance is inherently difficult, as most evaluations have been performed on individual and random partitions of the datasets. The model also had some failure when inspecting the F1-score, which could open the way to modify the model or to implement cross-validation. In this work, the proposed model opens the way for light neural network architecture to be implemented in the medical field, which is very severe and depends on high quality resources and performs well under these conditions (AUC result was good, averaging 0.811, accuracy 95%) and can be more implemented in future experiments. However, the sensitivity of the model is rather low, and it could raise the false negative classification of samples in the prediction. The low sensitivity rate is the result of the biased dataset (the difference in the class distribution). While the results obtained suggest that deep neural network training in the medical field is a viable choice, as more and more public databases become available, the practical application of deep learning in clinical practice remains an open topic. Especially for the ChestX-ray14 databases, the rather high tag noise of 10% makes it difficult to estimate the real network. Therefore, a clean test set without a label is necessary for a clinical efficiency evaluation. Future work may include the investigation of other model architectures, new architectures for leveraging label dependencies and the incorporation of segmentation information. Conceptualization, A.S., H.S. ; methodology, A.S., H.S. ; software, A.S, N.S. ; validation, A.S., H.S. and N.S. ; formal analysis, A.S.; investigation, A.S.; resources, A.S., N.S. ; data curation, A.S., N.S.