Cervical Cancer Detection: A Literature SurveyAuthor : V. Pushpalatha, S. Sathiamoorthy and M. Kamarasan
Volume 7 No.2 July-December 2018 pp 24-27
Cervical cancer is more common in women and worldwide it is most feared disease. Due to abnormal growth in the cervix cells, cervical cancer occurs and slowly it also spreads to the other organs of human body. Cervical cancer is caused by number reasons like human papilloma virus, using birth control pills, cigarette smoking, etc. In the initial stage, cervical cancer will not show any signs. However, if it is identified in earlier stage, it will be cured successfully. Nowadays, number of computer vision based approaches has been introduced to identify the cervical cancer disease and its stages. Still more research in this domain is ongoing towards getting high accuracy in the disease and stage prediction. In this paper, we studied a detailed literature on recognition of cervical cancer in connection with computer vision approaches.
Cervical Cancer, Denoising, Classification, Segmentation
 Mukherjee, Jhilam, Soharab H. Shaikh, MadhuchandaKar, and AmlanChakrabarti. “A Comparative Analysis of Image Segmentation Techniques toward Automatic Risk Prediction of Solitary Pulmonary Nodules”, 2016.
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 Mun z, N, F X. osch, and Ole M. Jensen. Human Papillomavirus and Cervical Cancer. Lyon: International Agency for Research on Cancer, 1989.
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 Mukhopadhyay, Sabyasachi, et al., “Optical diagnosis of cervical cancer by intrinsic mode functions”, Dynamics and Fluctuations in Biomedical Photonics XIV. Vol. 10063. International Society for Optics and Photonics, 2017.
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 Zhao, Lili, et al., “An efficient abnormal cervical cell detection system based on multi-instance extreme learning machine”, Ninth International Conference on Digital Image Processing (ICDIP 2017), International Society for Optics and Photonics, Vol. 10420, 2017.
 Manogaran, Gunasekaran, et al., “Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering”, Wireless personal communications, pp. 1-18, 2017.
 Bhargava, Ashmita, et al., “Computer Aided Diagnosis of Cervical Cancer Using HOG Features and Multi Classifiers”, Intelligent Communication, Control and Devices. Springer, Singapore, pp. 1491-1502, 2018.
 K. Hemalatha, and K. Usha Rani. “Feature Extraction of Cervical Pap Smear Images Using Fuzzy Edge Detection Method”, Data Engineering and Intelligent Computing. Springer, Singapore, pp. 83-90, 2018.
 Zhao, Lili, et al., “A novel unsupervised segmentation method for overlapping cervical cell images”, Ninth International Conference on Digital Image Processing (ICDIP 2017), International Society for Optics and Photonics, Vol. 10420, 2017.