Performance Comparison of SVM Classifier Based on Kernel Functions in Colposcopic Image Segmentation for Cervical Cancer

  • N. ThendralEmail author
  • D. Lakshmi
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Cervical cancer is the second most common cancer affecting women worldwide. It can be cured in almost all patients if detected and treated in time. Pap smear test has been broadly used for detection of cervical cancer. The conventional Pap smear test has several shortcomings including subjective nature, low sensitivity, and frequent retesting. In order to overcome this issue, colposcopy method is used for visual inspection of cervix with the aid of acetic acid and with proper magnification, abnormal cells to be identified. Thus, we propose a method for automatic cervical cancer detection using segmentation and classification. In this work, several methods used for detecting cervical cancer is discussed which uses different classification techniques like K-means clustering, texture classification and Support Vector Machine (SVM) to detect cervical cancer. The proposed work compares and determines accuracy for five types of kernel functions, namely Polynomial kernel, Quadratic kernel, RBF kernel, linear kernel, and Multi-Layer Perceptron kernel. Analysis shows that Multi-layer Perceptron kernel in SVM classifier provides the best performance with an accuracy of 98%.


Cervical cancer Support vector machine Multi-layer perceptron K-means clustering Accuracy 


  1. 1.
    Kashyap D et al (2016) Cervical cancer detection and classification using Independent Level sets and multi SVMs. In: 2016 39th international conference on telecommunications and signal processing (TSP), pp 523–528Google Scholar
  2. 2.
    Kaaviya S, Saranyadevi V, Nirmala M (2015) PAP smear image analysis for cervical cancer detection. In: 2015 IEEE International conference on engineering and technology (ICETECH), pp 1–4Google Scholar
  3. 3.
    Obukhova NA, Motyko AA, Kang U, Bae S-J, Lee D-S (2017) Automated image analysis in multispectral system for cervical cancer diagnostic. In: 2017 20th conference of open innovations association (FRUCT), pp 345–351Google Scholar
  4. 4.
    Njoroge E, Alty SR, Gani MR, Alkatib M (2006) Classification of cervical cancer cells using FTIR data. In: 2006 international conference of the ieee engineering in medicine and biology society, pp 5338–5341Google Scholar
  5. 5.
    Teeyapan K, Theera-Umpon N, Auephanwiriyakul S (2015) Application of support vector based methods for cervical cancer cell classification. In: 2015 IEEE international conference on control system, computing and engineering (ICCSCE), pp 514–519Google Scholar
  6. 6.
    Rajinikanth V, Dey N, Satapathy SC, Ashour AS (2018) An approach to examine magnetic resonance angiography based on Tsallis entropy and deformable snake model. Futur Gener Comput Syst 18:160–172CrossRefGoogle Scholar
  7. 7.
    Rajinikanth V,  Raja NSM, Kamalanand K (2017) Firefly algorithm assisted segmentation of tumor from brain MRI using Tsallis function and markov random field. Control Eng Appl Informatics 19(3):97–106Google Scholar
  8. 8.
    Karthick J, Lakshmi D (2016) A Sub-Threshold SRAM design for stability improvement in low power. Int J Emerg Trends Sci Technol 3(5):383–387Google Scholar
  9. 9.
    Muthumaheswaran N, Kabilamani P, Lakshmi D (2016) A high gain e-band power amplifier using 45 nm CMOS technology. Int J Emerg Trends Sci Technol 3(5):353–354Google Scholar
  10. 10.
    Lakshmi D, Roy S, Ranganathan H (2014) Automated texture based characterization of fibrosis and carcinoma using low-dose lung CT images. Int J Imaging Syst Technol 24(1):39–44Google Scholar
  11. 11.
    Lakshmi D, Roy S, Ranganathan H (2014) ANOVA of texture based feature set for lung tissue characterization using CT Images. J Comput Appl 7(1):1–5Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Information and CommunicationAnna UniversityChennaiIndia
  2. 2.Department of Electronics and CommunicationSt. Joseph’s College of EngineeringChennaiIndia

Personalised recommendations