## 4 Experimental Results
### Quantitative Evaluation  
In this scenario, the efficiency of the OGRU model is validated on the RSNA 2019 brain CT hemorrhage database utilizing recall, precision, f-measure, MCC, specificity, and accuracy. By viewing Tables 1 and 2, the performance of 7 different classifiers: LSTM, Deep Belief Network (DBN), autoencoder, Recurrent Neural Network (RNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), GRU, and OGRU, are tested with the Cuckoo Search Optimization (CSO) algorithm. During the classification, similar parameters are utilized in all runs for all classifiers and a five-fold cross-validation method is applied for analyzing the performance of the OGRU model where the better use of data for training and testing decreases the computational time with limited bias and variance. The OGRU model's performance is evaluated with two different training and testing percentages such as 50:50% and 80:20%. As stated in Table 1, the OGRU model with the CSO algorithm achieved 92.80% of precision, 90.28% of recall, 91.90% of f-measure, 92.91% of MCC, 90.88% of accuracy, and 90.48% of specificity in the intracranial hemorrhage detection with the 50% training and 50% testing of data. The obtained simulation results are better related to comparative classifiers such as LSTM, DBN, autoencoder, RNN, ANFIS, and the conventional GRU model. A graphical comparison of dissimilar classifiers with 50% training and 50% testing of data is depicted in Figure 7.  
Furthermore, the RSNA 2019 brain CT hemorrhage database is categorized into train set and test set with the ratio of 80% and 20%. In the RSNA 2019 brain CT hemorrhage database, the highest performance is achieved by the OGRU model with the CSO algorithm. The developed OGRU-CSO model achieved a precision of 99.86%, recall of 99.25%, f-measure of 99.34%, MCC of 99.67%, specificity of 99.40%, and classification accuracy of 99.36%, which are superior related to other classification techniques: LSTM, DBN, autoencoder, RNN, ANFIS, and conventional GRU model. By viewing Table 2, the proposed OGRU-CSO model is highly capable of extracting and optimizing the most discriminative features, which helps in achieving better classification with limited feature vectors. A graphical comparison of dissimilar classifiers with 80% training and 20% testing of data is stated in Figure 8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**CSO Algorithm with 50\% Training and 50\% Testing of Data**} \\ \hline
**Classifiers** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** & **MCC (\%)** & **Specificity (\%)** & **Accuracy (\%)** \\ \hline ANFIS & 78.30 & 74.90 & 81.81 & 80.80 & 72.90 & 74.82 \\ Autoencoder & 80.88 & 78.50 & 82.88 & 85.55 & 80 & 82.40 \\ RNN & 82.24 & 80.80 & 86.67 & 86.90 & 82.24 & 84.58 \\ DBN & 86.90 & 84.80 & 86.66 & 87.98 & 86 & 87.77 \\ LSTM & 82.34 & 87.78 & 87.20 & 88.90 & 87.40 & 88.90 \\ GRU & 88.90 & 88.68 & 88.50 & 90.18 & 88.90 & 90.08 \\ OGRU & 92.80 & 90.28 & 91.90 & 92.91 & 90.48 & 90.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation results of the proposed model by varying the classifiers with 50:50% training and testing of data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**CSO Algorithm with 80\% Training and 20\% Testing of Data**} \\ \hline
**Classifiers** & **Precision (\%)** & **Recall (\%)** & **F-Measure (\%)** & **MCC (\%)** & **Specificity (\%)** & **Accuracy (\%)** \\ \hline ANFIS & 94.50 & 94.09 & 93.40 & 90.83 & 92.30 & 94.32 \\ Autoencoder & 93.30 & 90.55 & 92.80 & 96.97 & 93.20 & 92.47 \\ RNN & 88.78 & 89.80 & 89.29 & 86 & 91.10 & 93.50 \\ DBN & 95.98 & 94.30 & 96.66 & 97.99 & 96.90 & 97.48 \\ LSTM & 97.90 & 97.74 & 97.82 & 98.94 & 97.43 & 98.93 \\ GRU & 98.98 & 98.65 & 98.50 & 99 & 98.98 & 99.04 \\ OGRU & 99.86 & 99.25 & 99.34 & 99.67 & 99.40 & 99.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation results of the proposed model by varying the classifiers with 80:20% training and testing of data.