A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer  
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1 Department of Information Technology, Sri Manakula Vinayagar Engineering College,  
Puducherry 605107, India 1  
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2 School of Computing, SASTRA Deemed University, Thanjavur 613401, India 2  
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3 Department of Computer Science, Pondicherry University, Karialak Campus, Puducherry 609605, India 3  
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3 Department of Computer Science, Pondicherry University, Karialak Campus, Puducherry 609605, India 3  
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4 Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International, Deemed University,  
Pune 412115, India 4  
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2 School of Computing, SASTRA Deemed University, Thanjavur 613401, India 2  
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5 Department of Computer Engineering, Sarvajank College of Engineering and Technology, Surat 39500, India 5  
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5 Department of Computer Engineering, Sarvajank College of Engineering and Technology, Surat 39500, India 5  
In contrast to the other cancer infectious categories, precursor lesions must be identified and treated by examining Pap smear cell tests [2]. Further, several automated cervical cancer detection schemes contributed to error minimization and productivity improvement during cervical cancer screening [3]. The considered automated cervical cancer detection schemes need to automatically differentiate between the normal and abnormal cells for a higher screening rate. The cytoplasm and nuclei morphological features are essential in distinguishing between normal and abnormal cells for a higher cervical cancer detection rate [4]. The most recent contributions in the relevant literature focus on the potential role of the nucleus in the process of cancer detection. Specifically, cervical cytoplasm investigation is vital in detecting all cervical cancer categories corresponding to squamous cell carcinoma, low-grade squamous cell intra-epithelial carcinoma, and accompanied nucleus abnormality categories [5]. Hence, reliable and automated detection of cervical cytoplasm and nuclei is essential for accurate screening of nucleus abnormalities categories of cervical cancer. This automated scheme also suffers from the limitations of the poor contrasting quality of cells and inappropriate cervical cell staining process that makes the extraction step of the cervical cells a complex issue in the detection phases. In addition, the Pap smear cells used for examination vary from thousands to ten thousand, leading to a complex investigation task. Moreover, most of the contributed automatic cervical cancer detection schemes focused on enhancing the classification accuracy rate, which in turn depends on the potential segmentation approach incorporated in the cervical cancer cell screening process [6]. Specifically, the segmentation of cervical Pap smear cells using a graph cut approach was identified to be remarkable in obtaining a good classification rate in the detection process of cervical cancer [7; 8; 9]. However, the Pap smear cells of cervical cancer are segmented using graph cut such as normalized graph cut and min-maximum cut set are determined to be incapable of resolving the problems which emerge because of the unstable staining, substandard quality and features that are contrasting of Pap smear cell images [10]. Hence, a significant detection of cervical cancer cells using the graph cut-based segmentation approach needs to be formulated to ensure an optimal classification accuracy rate. The proposed technique also helps identify the pre-cancerous cells so that the mortality rate in detecting cervical cancer can be reduced. The screening of cervical cancer is processed through the Pap smear test or liquid cytology-based lesions. The features of the cells, such as texture, color, size and nucleus cytoplasm (n/c) ratio, are extracted and classified with the severity of the stage in cancer so that it can be treated at the earliest.