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 very common in women, and it is the most dreaded disease. Cervical cancer if detected early can be treated successfully. Cervical cancer occurs due to the uncontrolled growth of the cells present in the cervix of the female body, and it also occurs due to the virus human papilloma virus (HPV). Pathologists diagnose cervical cancer by a screening test called Papanicolaou test or Pap smear test. The pap smear test is not always 100% accurate but it helps in early detection of cancerous cells. This paper describes the literature survey on the detection of Cervical cancer by using image processing approaches.
Cervical cancer, Image Processing, Feature Extraction, Pre-processing, Classification, Clustering
 Mukherjee, Jhilam, Soharab H. Shaikh, MadhuchandaKar and AmlanChakrabarti, “A Comparative Analysis of Image Segmentation Techniques toward Automatic Risk Prediction of Solitary Pulmonary Nodules”, 2016.
 S. Athinarayanan and M. V. Srinath, “Classification of Cervical Cancer Cells in Pap Smear Screening Test”, Ictact Journal on Image and Video Processing, Vol. 6, No. 4, pp. 1234-1238, 2016.
 Mun z, N, F X. osch and Ole M. Jensen, Human Papillomavirus and Cervical Cancer, Lyon: International Agency for Research on Cancer, 1989.
 RanuGorai, “A Survey on Digital Image Processing”, International Journal of Research in Engineering, Technology and Science, pp. 1-7, 2016.
 Xu, Tao, et al. “Multi-feature based benchmark for cervical dysplasia classification evaluation”, Pattern Recognition, Vol. 63, pp. 468-475, 2017.
 Taneja, Arti, PriyaRanjan and AmitUjlayan. “Multi-cell nuclei segmentation in cervical cancer images by integrated feature vectors”, Multimedia Tools and Applications Vol. 77, No. 8, pp. 9271-9290, 2018.
 D.Selvathi, W. RehanSharmila and P. ShenbagaSankari, “Advanced Computational Intelligence Techniques Based Computer Aided Diagnosis System for Cervical Cancer Detection Using Pap Smear Images”, Classification in BioApps.Springer, Cham, pp. 295-322, 2018.
 Bora, Kangkana, et al. “Automated classification of Pap smear images to detect cervical dysplasia”, Computer methods and programs in biomedicine Vol. 138, pp. 31-47, 2017.
 Iliyasu, M. Abdullah, and ChastineFatichah, “A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection”, Sensors, Vol. 17, No. 12, pp. 2935, 2017.
 Singh, Sanjay Kumar and Anjali Goyal. “A Novel Approach to Segment Nucleus of Uterine Cervix Pap Smear Cells Using Watershed Segmentation”, Advanced Informatics for Computing Research. Springer, Singapore, 2017. pp. 164-174.
 William, Wasswa, et al. “A review of Image Analysis and Machine Learning Techniques for Automated Cervical Cancer Screening from pap-smear images”, Computer Methods and Programs in Biomedicine, 2018.
 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.
 Kudva, Vidya, Keerthana Prasad and ShyamalGuruvare, “Detection of specular reflection and segmentation of cervix region in uterine cervix images for cervical cancer screening”, IRBM, Vol. 38, No. 5, pp. 281-291, 2017.
 Sornapudi, Sudhir, et al. “Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels”, Journal of Pathology Informatics, Vol. 9, 2018.
 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).Vol. 10420, International Society for Optics and Photonics, 2017.
 anogaran, 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), Vol. 10420, International Society for Optics and Photonics, 2017.