Detection of Cervical Cancer Based on Learning Vector Quantization and Wavelet Transform

Authors

  • Dhimas Arief Dharmawan Universitas Muhammadiyah Yogyakarta http://orcid.org/0000-0003-1971-3214
  • Latifah Listyalina Department of Electrical Engineering, Faculty of Scince and Technology, Universitas Respati Yogyakarta

DOI:

https://doi.org/10.18196/jet.3357

Keywords:

Cervical Cancer, Images, Papsmear, Wavelet

Abstract

Cervical cancer has became the common women dsease in the world. Mostly, cervical cancer has been already known lately, because it is very dificult to detect this in early stage. In this work, a computer based software using Learning Vector Quantization (LVQ) has been designed as the early cervical cancer detection aid tool. There are six methods before the detection is performed, namely preprocessing, contrast stretching, median filtering, morphology operation, image segmentation, and Wavelet Transform based feature extraction. In tihis work, 73 cervical cell images that consist of 50 normal images and 23 cancer images are used. 35 normal images and 14 cancer images are used to train the LVQ. Then, 23 normal images and 9 cancer images are used in the testing process. Our results show 88,89 % cancer image can be detected correctly (sensitivity), 100 % normal image can be detected corerctly (specificity), and 95,83 % for overall detection (accuracy).

References

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https://www.eecis.udel.edu/~amer/CISC651/IEEEwavelet.pdf

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Published

2019-09-18

How to Cite

Dharmawan, D. A., & Listyalina, L. (2019). Detection of Cervical Cancer Based on Learning Vector Quantization and Wavelet Transform. Journal of Electrical Technology UMY, 3(3), 78–82. https://doi.org/10.18196/jet.3357

Issue

Vol. 3 No. 3 (2019): September

Section

Articles

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