Automatic Approach for Cervical Cancer Detection and Segmentation Using Neural Network Classifier

Document Type : Research Articles


1 Department of Ece, Kongunadu College of Engineering and Technology, Trichy, Tamilnadu, India.

2 Head of Ece, Mahendra College of Engineering, Salem, India.


Cervical cancer leads to major death disease in women around the world every year. This cancer can be cured if it
is initially screened and giving timely treatment to the patients. This paper proposes a novel methodology for screening
the cervical cancer using cervigram images. Oriented Local Histogram Technique (OLHT) is applied on the cervical
image to enhance the edges and then Dual Tree Complex Wavelet Transform (DT-CWT) is applied on it to obtain
multi resolution image. Then, features as wavelet, Grey Level Co-occurrence Matrix (GLCM), moment invariant
and Local Binary Pattern (LBP) features are extracted from this transformed multi resolution cervical image. These
extracted features are trained and also tested by feed forward back propagation neural network to classify the given
cervical image into normal and abnormal. The morphological operations are applied on the abnormal cervical image
to detect and segment the cancer region. The performance of the proposed cervical cancer detection system is analyzed
in the terms of sensitivity, specificity, accuracy, positive predictive value, negative predictive value, Likelihood Ratio
positive, Likelihood ratio negative, precision, false positive rate and false negative rate. The performance measures for
the cervical cancer detection system achieves 97.42% of sensitivity, 99.36% of specificity, 98.29% of accuracy, PPV
of 97.28%, NPV of 92.17%, LRP of 141.71, LRN of 0.0936, 97.38 % precision, 96.72% FPR and 91.36% NPR. From
the simulation results, the proposed methodology outperforms the conventional methodologies for cervical cancer
detection and segmentation process.


Main Subjects

Volume 19, Issue 12
December 2018
Pages 3571-3580
  • Receive Date: 30 May 2018
  • Revise Date: 21 September 2018
  • Accept Date: 02 November 2018
  • First Publish Date: 01 December 2018