Total views : 373

Classification of Clinical Dataset of Cervical Cancer using KNN

Affiliations

  • School of Computer Science Engineering, Lovely Professional University, Phagwara - 144411, Punjab, India

Abstract


Background/Objectives: The primary objective of this paper is to classify the clinical dataset of cervical cancer to identify the stage of cancer which helps in proper treatment of patient suffering from cancer. Methods/Statistical Analysis: This research work basically moves toward the detection of cervical cancer using Pap smear images. Analysis of Pap smear of cervical region is an efficient technique to study any abnormality in cervical cells. The proposed system firstly segment the pap image using Edge Detection to separate the cell nuclei from cytoplasm and background and then extract various features of cervical pap images like area, perimeter, elongation and then these features are normalized using min-max method. After normalization KNN method is used to classify cancer according to its abnormality. Findings: The classification accuracy with 84.3% of maximum performance with no validation and classification accuracy with 82.9% of maximum performance with 5 Fold cross validation is achieved.

Keywords

Cervical Cancer, Cell Images, Classification, Pap Smear Test.

Full Text:

 |  (PDF views: 468)

References


  • American Cancer Society. Cancer Facts and Figures. 2015. Available from: http://www.cancer.org/acs/groups/cid/documents/webcontent/003094-pdf.pdf
  • Papanicolaou GN. The cell smears method of diagnosing cancer. American Journal of Public Health. 1948 Feb; 38(2):202–5.
  • Byriel J. Neuro-fuzzy classification of cells in cervical smears. Denmark, Oersted. 1999; 8(1):38.
  • Plissiti ME, Charchanti A. Automated segmentation of cell nuclei in PAP smear images. ITAB Proceedings of International Special Topic Conference on Information Technology in Bio Medicine; Greece, Ioannina. 2006.
  • Ampazis N, Dounias G, Jantzen J. Pap-smear classification using efficient second order neural network training algorithms. Lecture Notes in Artificial Intelligence; 2004 May. p. 230–45.
  • Othman NH. Capability of new features of cervical cells for cervical cancer diagnostic system using hierarchical neural network. IJSSST. 2008; 9(2).
  • Malm P, Balakrishnan N, Sujathan VK, Kumar R, Bengtsson E. Debris removal in Pap-smear images. Computer Methods and Programs in Biomedicine. 2013 Jul; 111(1):128–38.
  • Lezoray O, Cardot H. Cooperation of color pixel classification schemes and color watershed: A study for microscopic images. IEEE Trans Image Process. 2002 Jul; 11(7):783-9.
  • Begelman G, Gur E. Cell nuclei segmentation using fuzzy logic engine. ICIP Proceedings of International Conference on Image Processing; 2004 Nov. p. 2937–40.
  • Hiremath PS. Fuzzy Rule based classification of microscopic images of squamous cell carcinoma of esophagus. International Journal of Computer Application. 2011 Jul; 25(11):30- 3.
  • Lee KM, Street WN. Learning shapes for automatic image segmentation. Proceedings of INFORMS-KORMS Conference; Seoul Korea. 2000 Jun. p. 1461-8.
  • Bamford P, Lovell B. A water immersion algorithm for cytological image segmentation. Proceedings of the APRS Image Segmentation Workshop; 1996. p. 75-9.
  • Costa JAF, Mascarenhas NDA. Society for optical engineering cell nuclei segmentation in noisy images using morphological watersheds. International Proceedings on PIE of 3164; 1997. p. 314-24.
  • Mouroutis T, Roberts S, Robust J. Cell nuclei segmentation using statistical modeling. IOP Bioimaging. 1998 Jun; 6(2):79-91.
  • Bamford P, Lovell B. Unsupervised cell nucleus segmentation with active contours. Signal Processing. 1998 Dec; 71(2):203-13.
  • Garrido A, Blanca PDLN. Applying deformable templates for cell image segmentation. Pattern Recognition. 2000; 33(5):821-32.
  • Yi-Wei Y, Jung-Hua W. Image segmentation based on region growing and edge detection. IEEE SMC Conference on Systems, Man and Cybernetics; 1999 Oct. p. 798-803.
  • Gonzalez W. Digital Image Processing using Matlab. Gatesmark Publishing; 2009. p. 827.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.