## 4 Experiment Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers**} & \multicolumn{5}{c}{**Performance Measures**} \\ \cline{2-7}  & **Sensitivity** & **Specificity** & **Precision** & **F Score** & **Accuracy** & **G Mean** \\ \hline NB & 62.07\% & 98.68\% & 88.89\% & 73.10\% & 93.38\% & 78.26\% \\ SVM & 78.45\% & 97.51\% & 84.26\% & 81.25\% & 94.75\% & 87.46\% \\ KNN & 79.31\% & 97.66\% & 85.19\% & 82.14\% & 95.00\% & 88.01\% \\ DT & 66.67\% & 97.07\% & 79.59\% & 72.56\% & 92.63\% & 80.45\% \\ RF & 84.48\% & 97.51\% & 85.22\% & 84.85\% & 95.63\% & 90.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of classification accuracy obtained through different classifiers using all features. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Feature Name** & **Category** \\ \hline
1. & Energy & Textural Feature \\
2. & Variance & Textural Feature \\
3. & Equivalent Circular Diameter to actual diameter & Geometric Feature \\
4. & Eccentricity & Geometric Feature \\
5. & Ratio between area and perimeter & Geometric Feature \\
6. & Entropy of Local standard deviation filtered Image & Textural Feature \\
7. & Actual Diameter & Geometric Feature \\
8. & Entropy & Textural Feature \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Features selected using random forest ensemble feature selection. Figure 16: Receiver operating characteristics (ROC) curves: graphical performance evaluation of different classifiers using selected features by RF. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers**} & \multicolumn{5}{c}{**Performance Measures**} \\ \cline{2-7}  & **Sensitivity** & **Specificity** & **Precision** & **F Score** & **Accuracy** & **G Mean** \\ \hline NB & 52.14\% & 97.51\% & 78.21\% & 62.56\% & 90.88\% & 71.30\% \\ SVM & 93.16\% & 97.22\% & 85.16\% & 88.98\% & 96.63\% & 95.17\% \\ KNN & 90.60\% & 98.24\% & 89.83\% & 90.21\% & 97.13\% & 94.34\% \\ DT & 65.52\% & 98.68\% & 89.41\% & 75.62\% & 93.88\% & 80.41\% \\ RF & 96.58\% & 98.68\% & 92.62\% & 94.56\% & 98.38\% & 97.63\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of classification accuracy obtained through different classifiers using RF’s selected features. In order to compare with previous studies, there was no common dataset, and previous studies were evaluated based on different types of images. In order to make a fair, objective comparison, we adopted the methodologies of previous studies to our application. It should be noted that all the methods in the comparison were evaluated with the same experiment settings and the same dataset used to test the proposed method. Thus, the evaluation results were compared fairly without affecting any other factors. The comparison of classification accuracy obtained along with their corresponding methodologies is presented in Table 8. From the experiment results, it is inferred that our proposed method provides superior accuracy compared to previous works [13; 17]. It is also reasonable to conclude that our combination of geometric and textural features is more discriminant than the features used in previous studies for classifying single and overlapping nuclei. To validate computational efficiency, we also analyzed the processing time of each processing step and the entire algorithm as given in Table 9 and found that computational complexity is relatively simple. The visual results of detected overlapped nuclei using our proposed method are depicted in Figure 18.