# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 5 Conclusions and Future Work  
The early diagnosis and treatment of lung disease are essential to prevent deaths worldwide. In this study, we propose a novel framework to integrate multiple features from both shallow and deep features. Representative and discriminative features are obtained to distinguish 14 pathologies from the public ChestX-ray14 dataset after conducting comprehensive experiments. We were able to generate the disease heat-maps despite having a limited number of annotated bounding boxes of  
Figure 6: Classification results using the integration of all features (i.e., FI 3) among the classifiers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pathology** & **[PERSON] et al. [4]** & **[PERSON] et al. [41]** & **[PERSON] et al. [61]** & **[PERSON] et al. [44]** & **Proposed** \\ \hline Atelectasis & 0.716 & 0.772 & 0.767 & 0.743 & **0.795** \\ Cardiomegaly & 0.807 & **0.904** & 0.883 & 0.875 & 0.887 \\ Effusion & 0.784 & 0.859 & 0.828 & 0.811 & **0.875** \\ Infiltration & 0.609 & 0.695 & **0.709** & 0.677 & 0.703 \\ Mass & 0.706 & 0.792 & 0.821 & 0.783 & **0.835** \\ Nodule & 0.671 & 0.717 & **0.758** & 0.698 & 0.716 \\ Pneumonia & 0.633 & 0.713 & 0.731 & 0.696 & **0.742** \\ Pneumothorax & 0.806 & 0.841 & 0.846 & 0.810 & **0.863** \\ Consolidation & 0.708 & **0.788** & 0.745 & 0.723 & 0.786 \\ Edema & 0.835 & 0.882 & 0.835 & 0.833 & **0.892** \\ Emphysema & 0.815 & 0.829 & **0.895** & 0.822 & 0.875 \\ Fibrosis & 0.769 & 0.767 & **0.818** & 0.804 & 0.756 \\ Pleural Thickening & 0.708 & 0.765 & 0.761 & 0.751 & **0.774** \\ Hernia & 0.767 & **0.914** & 0.896 & 0.899 & 0.836 \\ Average & 0.738 & 0.801 & 0.807 & 0.781 & **0.8097** \\ \hline \hline \end{tabular}
\end{table}
Table 3: AUC comparison of the 14 pathologies classification in the ChestX-ray14 literature.  
pathologies. In addition, our approach yielded competitive classification accuracies compared with the baseline approach. In the future, we will focus on exploiting different shallow feature descriptors and deep features, learning the feature representation, and incorporating them in an end-to-end manner to improve the classification performance.  
The work described in this article is the collaborative development of all authors. [PERSON] contributed to the idea of data processing and designed the algorithm. [PERSON] made contributions to data processing and analysis. [PERSON] and [PERSON] participated in the writing of the paper.  
This work was supported by the Brain Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (NRF-2019M3C7A1020406), the Engineering Research Center (ERC) Program of Extreme Exploitation of Dark Data through the Korean Government (MSIT) (NRF-2018R1A5A1060031) and the Basic Science Research Program through the NRF, funded by the Ministry of Education (NRF-2017R1D1A1B03036423).  
The authors would like to note that some part of work was done when [PERSON] was in Seoul National University Hospital, and we express our gratitude for the valuable and constructive comments made by [PERSON], [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.