## 4 Experimental Results
### Quantitative Evaluation
Figure 7: Graphical comparison of dissimilar classifiers with 50% training and 50% testing of data. In Tables 3 and 4, the performance investigation is conducted by using different feature optimization algorithms, such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), CSO, and Grasshopper Optimization Algorithm (GOA) with OGRU model. Related to other combinations, the CSO algorithm with the OGRU attained higher results in intracranial hemorrhage detection. In this paper, the CSO algorithm significantly selects the optimum feature vectors with a better balance between exploitation and exploration. However, the comparative optimization algorithms suffer from a premature convergence rate because the optimization algorithms are stuck at the local optimal value. As seen in Tables 3 and 4, the proposed OGRU-CSO model obtained significant performance with 80:20% training and testing of RSNA 2019 brain CT hemorrhage data compared to 50:50% training and testing of data. The graphical representation of dissimilar optimizers with 50% training and 50% testing of data is specified in Figure 9. Correspondingly, the graphical comparison of dissimilar optimizers with 80% training and 20% testing of data is denoted in Figure 10. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**OGRU Model with 80% Training and 20% Testing of Data**} \\ \hline
**Optimizers** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** & **MCC (\%)** & **Specificity (\%)** & **Accuracy (\%)** \\ \hline ACO & 91.40 & 90.90 & 90.00 & 93.80 & 90.40 & 92.30 \\ ABC & 92.50 & 90.50 & 93.90 & 97.90 & 95.90 & 94.40 \\ PSO & 89.70 & 93.50 & 95.85 & 93.29 & 94.67 & 95.55 \\ GA & 94.00 & 94.56 & 97.65 & 97.80 & 97.65 & 96.40 \\ WOA & 96.70 & 96.90 & 98.80 & 98.70 & 98.40 & 98.99 \\ GOA & 98.90 & 97.69 & 98.97 & 99.09 & 99.00 & 99.11 \\ CSO & 99.86 & 99.25 & 99.34 & 99.67 & 99.40 & 99.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation results of the proposed model by varying the optimizers with 80:20% training and testing of data. Figure 8: Graphical comparison of dissimilar classifiers with 80% training and 20% testing of data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**OGRU Model With 50% Training and 50% Testing of Data**} \\ \hline
**Optimizers** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** & **MCC (\%)** & **Specificity (\%)** & **Accuracy (\%)** \\ \hline ACO & 78.90 & 80.10 & 79.00 & 78.00 & 80.54 & 82.36 \\ ABC & 82.50 & 80.55 & 80.92 & 77.97 & 85.96 & 84.45 \\ PSO & 84.66 & 82.20 & 81.82 & 78.20 & 84.68 & 85.59 \\ GA & 88.70 & 84.58 & 84.73 & 79.84 & 87.68 & 86.48 \\ WOA & 88.78 & 86.94 & 85.86 & 80.50 & 88.80 & 88.87 \\ GOA & 88.90 & 87.87 & 88.96 & 82.90 & 89.90 & 89.19 \\ CSO & 92.80 & 90.28 & 91.90 & 92.91 & 90.48 & 90.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results of the proposed model by varying the optimizers with 50:50% training and testing of data. In addition, the simulation results of the proposed model by varying the features with 50:50% and 80:20% training and testing of data are depicted in Tables 5 and 6. By inspecting the tables, the hybrid-feature extraction has achieved better classification results compared to individual feature descriptors by means of precision, recall, f-measure, MCC, specificity, and accuracy. A graphical comparison of dissimilar features with 50% training and 50% testing of data is denoted in Figure 11. In addition, the graphical comparison of dissimilar features with 80% training and 20% testing of data is represented in Figure 12.