## 4 Experimental Results  
In this section, the proposed HLC-BC-GCST scheme towards an effective segmentation process of cytoplasm and nuclei boundaries in cervical cancer detection is evaluated and compared with the existing baseline techniques GLGC-CCDT, IOTSU-CCDT, PT-KMS-CCDT and NAMSC-CCDT using the Herlev dataset. The Herlev dataset is mainly used in investigating the proposed HLC-BC-GCST scheme because it is the predominant dataset used in most automated schemes proposed for effectively detecting cervical cancer. This popular Herlev dataset consists of approximately 917 cervical Pap smear cells gathered and stored in the Herlev University Hospital over the years. This Herlev dataset contains seven types of cervical Pap smear cells corresponding to severe dysplasia, mild dysplasia, moderate dysplasia, intermediate epithelium, columnar epithelium, and the superficial and columnar intensity of infected cells. More specifically, approximately 146, 150, 182 and 192 cervical cell images out of the complete 917 cervical moderate dysplasia and carcinoma in nature. Matlab 2013 is used to investigate the proposed HLC-BC-GCST scheme over the benchmarked GLGC-CCDT, IOTSU-CCDT, PT-KMS-CCDT and NAMSC-CCDT techniques for evaluating its performance. Cell images are referred to as severe dysplasia and mild dysplasia using the Herlev dataset (Figures 1 and 2). Figure 1: From left to right images represents the carcinoma cell from the Herlev dataset where the input image is given in CIE Lab colour space, and where initially the linear iterative clustering is applied to separate the regions of nucleus and cytoplasm, the optimized graph cut is generating the hyper pixels, and a Bayesian classifier is used to classify the boundaries of nucleus and cytoplasm where the high intensity darker pixels are regions of a nucleus and the red boundary marked is the region of the cytoplasm. Figures 1 and 2 explain the potential of the HLC-BC-GCST scheme evaluated using classification accuracy and sensitivity under varying rounds in the implementation process. The classification accuracy of the proposed HLC-BC-GCST scheme is determined to be 99.51%. This classification accuracy of 99.51% facilitated by the proposed HLC-BC-GCST scheme is determined to be 3.56% excellent compared to the existing benchmarked GLGC-CCDT, IOTSU-CCDT, PT-KMS-CCDT and NAMSC-CCDT techniques used for comparison. This improved classification accuracy rate was made feasible by using the Bayes classifier for error minimization and multiple applications of the SLICP process during the segmentation process. Similarly, the HLC-BC-GCS scheme obtains a sensitivity that is determined to be 98.81%. This sensitivity rate of 98.81% enabled by the proposed HLC-BC-GCST scheme is determined to be 4.32% predominant baseline GLGC-CCDT, IOTSU-CCDT, PT-KMS-CCDT and NAMSC-CCDT techniques are analyzed for comparison. The enhanced sensitivity rate in the proposed HLC-BC-GCST scheme is mainly due to the incorporation of multiple GrabCut-based graph-cut approaches and potential estimation of GMM parameters during the process of segmenting the cytoplasm and nucleus boundaries that aids in cervical cancer detection. The representation of their cells in the graph cut is presented in Figure 3. Figures 4 and 5 presents the performance of the proposed HLC-BC-GCST scheme evaluated using classification accuracy and sensitivity under a varying number of rounds in the implementation process. Figures 6 and 7 depict the performance of the proposed HLC-BC-GCST scheme evaluated using specificity and mean processing time incurred per image under various rounds in the implementation process. The specificity of the proposed HLC-BC-GCST scheme is determined to be 99.11%. This specificity of the proposed HLC-BC-GCST scheme is 4.13% excellent compared to the existing benchmarked GLGC-CCDT, IOTSU-CCDT, PT-KMS-CCDT and NAMSC-CCDT techniques used for comparison. The mean processing time incurred by the proposed image of the HLC-BC-GCST scheme is 2.26 milliseconds.