# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 4 Experimental Results
### Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**Dataset Division** (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}} \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{}}}}}}}}}}}}}}}\)}} & & & \multicolumn{4}{c}{**Accuracy**} \\ \cline{5-8}  & & **(\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}\))** & **(\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}}}\))** & **(\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}}}\))** & **(\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}}}\)} \\ \cline{3-8}  & & & & & & & \\ \hline \multirow{11}{*}{F1} & \multirow{2}{*}{SIFT+GIST+LBP+HOG} & \multirow{2}{*}{Training:} & 0.5720 & 0.6026 & 0.5562 & 0.5598 & 0.6451 & 0.6621 & 0.7669 \\ \cline{2-8}  & & GIST & & 0.5850 & 0.6030 & 0.6557 & 0.5732 & 0.6401 & 0.6710 & 0.7227 \\ \hline \multirow{11}{*}{F1} & LBP & \multirow{2}{*}{Training:} & 0.5941 & 0.6009 & 0.6440 & 0.5865 & 0.6471 & 0.7210 & 0.7828 \\ \cline{2-8}  & & HOG & & 0.5947 & 0.6012 & 0.6487 & 0.5897 & 0.6489 & 0.6751 & 0.7837 \\ \cline{2-8}  & & SIFT+GIST+LBP+HOG & & & **0.6039** & **0.6205** & **0.6587** & **0.6112** & **0.6551** & **0.6902** & **0.7941** \\ \cline{2-8}  & & Conv5+SIFT & & 0.5753 & 0.5569 & 0.4803 & 0.6065 & 0.6401 & 0.6452 & 0.6742 \\ \cline{2-8}  & & Conv5+LBP & & 0.6006 & 0.5599 & 0.4808 & 0.6128 & 0.6451 & 0.6309 & 0.7115 \\ \cline{2-8}  & & Conv5+LBP & & 0.6058 & 0.5607 & 0.4817 & 0.6213 & 0.6519 & 0.6649 & 0.8115 \\ \cline{2-8}  & & Conv5+HOG & & 0.5901 & 0.5577 & 0.4807 & 0.5992 & 0.6551 & 0.6501 & 0.8105 \\ \cline{2-8}  & & & **0.6834** & **0.6332** & **0.6052** & **0.7478** & **0.7952** & **0.7701** & **0.8462** \\ \hline \multirow{11}{*}{F1} & \multirow{2}{*}{Conv5+SIFT+GIST+LBP+HOG} & \multirow{2}{*}{Training:} & \multirow{2}{*}{70\% (78,484)} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ \cline{1-1} \cline{5-8}  & & Validation: & & & & & & & \\ \cline{1-1} \cline{5-8}  & & 10\% (11,212) & & & & & & & \\ \cline{1-1} \cline{5-8}  & & & Testing: & & & & & & \\ \cline{1-1} \cline{5-8}  & & & 20\% (22,424) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracies of the classifiers using the feature(s) w/ or w/o the feature integration approach.