## 4 Experimental Results
This mean processing time of the proposed HLC-BC-GCST is obtained by 3.78%, remarkably compared to the GLGC-CCDT, IOTSU-CCDT, PT-KMS-CCDT and NAMSC-CCDT techniques used for comparison. This improved rate of specificity and mean processing rate  
Figure 2: From left to right images represents the moderate dysplasia cell from the Herlev database, where the segmentation of nucleus and cytoplasm regions is performed using the simple iterative clustering initially, where the regions are separated, an optimized GrabCut is used for the hyper pixels generation with a Gaussian mixture model, and finally, the Bayes classifier is used to identify the nucleus and cytoplasm regions, where the dark intensity regions are represented as a nucleus and the boundary marked in red is identified as cytoplasm. Figures 8 and 9 highlight the significance of the proposed HLC-BC-GCST scheme evaluated using precision and recall values under the varying number of rounds in the implementation process. The precision value of this proposed HLC-BC-GCST scheme is estimated as 0.96 \(\pm\) 0.19, which is enhanced to a maximum level of 4.97%, remarkable to the baseline GLGC-CCDT IOTSU-CCDT, PT-KMS-CCDT and NAMSC-CCDT techniques. In particular, the precision values of the proposed HLC-BC-GCST scheme are determined to be 0.96 \(\pm\) 0.03, 0.95 \(\pm\) 0.12, 0.94 \(\pm\) 0.08 and 0.94 \(\pm\) 0.18 under cervical images of severe dysplasia, moderate dysplasia, mild dysplasia and carcinoma used for analysis. Likewise, the recall value in the HLC-BC-GCST scheme is estimated as 0.97 \(\pm\) 0.11, which is enhanced to a maximum level of 5.12% predominant to the baseline GLGC-CCDT, IOTSU-CCDT, PT-KMS-CCDT and NAMSC-CCDT techniques. In particular, the recall value in HLC-BC-GCST is determined by 0.97 \(\pm\) 0.15, 0.96 \(\pm\) 0.13, 0.95 \(\pm\) 0.05 and 0.95 \(\pm\) 0.17 under cervical images of severe dysplasia, moderate dysplasia, mild dysplasia and carcinoma are analyzed for investigation. Figure 6: The proposed HLC-BC-GCST scheme evaluated using specificity. Figure 7: The proposed HLC-BC-GCST scheme evaluated using mean processing time. Tables 1-3 highlight the performance of the proposed HLC-BC-GCST scheme, where different metrics are used to compare and evaluate the existing systems' performance over the proposed system. The results obtained by the proposed system show an increase in the overall performance accuracy of the detection of cervical cancer. \begin{table}
\begin{tabular}{c c c} \hline \hline
**First Author and Year** & \
\begin{tabular}{c} **Classification** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Specificity (\%)** \\ \end{tabular} \\ \hline _Proposed HLC-BC-GCST_ & **99.51** & **99.11** \\ [PERSON], 2008 [13] & 98.56 & 98.13 \\ [PERSON], 2009 [14] & 98.73 & 98.24 \\ [PERSON], 2014 [15] & 98.92 & 97.86 \\ [PERSON], 2014 [16] & 98.43 & 97.83 \\ [PERSON], 2018 [21] & 98.32 & 97.84 \\ [PERSON], 2018 [21] & 98.55 & 97.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative Performance: Accuracy and Specificity. Figure 8: The proposed HLC-BC-GCST scheme evaluated using a precision value. Figure 9: Proposed HLC-BC-GCST scheme evaluated using recall value. The summarized results in Tables 1-3 confirm that the classification accuracy, specificity, sensitivity, mean processing time, precision, and recall value of the proposed HLC-BC-GCST scheme are better in mean rate of 3.21%, 4.52%, 3.84%, 0.82 s, 3.42% and 4.12% that are excellent compared to the cervical cancer detection approaches detailed in the related work section.