## 3 Experimental Results

Table 3: Classification results based on hybrid simulated annealing coupling with different classifiers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifiers** & **Sensitivity** & **Specificity** & **Accuracy** & **Precision** & **F-measure** \\ \hline SVM & 72.72\% & 96.21\% & 90.83\% & 85.10\% & 78.43\% \\ DT & 81.81\% & 97.29\% & 93.75\% & 90.00\% & 85.71\% \\ LR & 47.27\% & 94.05\% & 83.33\% & 70.72\% & 56.52\% \\ LDA & 94.54\% & 65.40\% & 72.08\% & 44.82\% & 60,82\% \\ KNN & 83.63\% & 91.89\% & 90.00\% & 75.41\% & 79.31\% \\ EB & 85.45\% & 97.83\% & 95.00\% & 92.16\% & 88.68\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results based on genetic algorithm coupling with different classifiers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifiers** & **Sensitivity** & **Specificity** & **Accuracy** & **Precision** & **F-measure** \\ \hline SVM & 90.90\% & 95.13\% & 94.16\% & 84.75\% & 87.72\% \\ DT & 89.09\% & 95.67\% & 94.16\% & 85.96\% & 87.50\% \\ LR & 54.54\% & 96.76\% & 87.08\% & 83.33\% & 65.93\% \\ LDA & 92.72\% & 68.64\% & 74.16\% & 46.79\% & 62.19\% \\ KNN & 90.90\% & 94.40\% & 93.33\% & 81.97\% & 86.20\% \\ EB & 90.90\% & 97.83\% & 96.25\% & 92.59\% & 91.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results based on particle swarm optimization coupling with different classifiers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **No. of Images** & \multicolumn{2}{c}{**Methods/Input Features/Classifiers**} & **Performance** \\ \hline \multirow{6}{*}{[PERSON] et al. 2009 [10]} & \multirow{6}{*}{1000} & – & Features of the sub-images locally & \multirow{6}{*}{Sen = 92.00\%} \\  & & – & Features extraction using 9 different & \\  & & – & sets of features & \\  & & – & Multiple classifiers & \\ \hline \multirow{6}{*}{[PERSON] et al., 2015 [9]} & \multirow{6}{*}{930} & – & Morphological Component Analysis & \multirow{6}{*}{Acc = 92.82\%} \\  & & – & Features extraction using & \\  & & – & Statistical features & \\  & & – & Support vector machine & \\ \hline \multirow{6}{*}{[PERSON] et al., 2016 [6]} & \multirow{6}{*}{1344} & – & Intensity transformation and \multirow{6}{*}{Sen = 80\%} \\  & & – & multi-level histogram analysis & \\ \cline{1-1}  & & – & A waterfall model based & \\ \cline{1-1}  & & – & classification approach & \\ \hline \multirow{6}{*}{[PERSON] et al., 2016 [4]} & \multirow{6}{*}{800} & – & Bi-dimensional empirical mode & \multirow{6}{*}{Acc = 88.63\%} \\  & & – & decomposition (BEMD) & \\ \cline{1-1}  & & – & Features extraction using 2D Intrinsic & \multirow{6}{*}{Sen = 86.25\%} \\ \cline{1-1}  & & – & Mode Functions and residue & \\ \cline{1-1}  & & – & Feature raking & \\ \cline{1-1}  & & – & Support vector machine & \\ \hline \multirow{6}{*}{[PERSON] et al., 2017 [5]} & \multirow{6}{*}{1486} & – & 2-D Continuous Wavelet & \multirow{6}{*}{Sen = 99.37\%} \\  & & – & Transform(2D-CWT) & \\ \cline{1-1}  & & – & Features extraction using scalogram & \\ \cline{1-1}  & & images of 2D-CWT & & \\ \cline{1-1}  & & – & Synthetic data using ADASYN & \multirow{6}{*}{Acc = 92.48\%} \\ \cline{1-1}  & & – & Particle swarm optimization & \\ \cline{1-1}  & & – & Random forest classifier & \\ \hline \multirow{6}{*}{Proposed method} & \multirow{6}{*}{1200} & – & Kirsch edge detection and & \multirow{6}{*}{Sen = 90.90\%} \\  & & & morphological operation & \\ \cline{1-1}  & & – & Features extraction using eight & \multirow{6}{*}{Acc = 97.08\%} \\ \cline{1-1}  & & feature extractors & & \\ \cline{1-1}  & & – & Hybrid simulated annealing & \\ \cline{1-1}  & & – & Ensemble bagging classifier & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Area under receiver operating characteristic curve of different pairs of feature selection and classifiers.