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Validation of Performance Homogeneity of Chan-Vese Model on Selected Tumour Cells

Author

Listed:
  • Justice Kwame Appati

    (University of Ghana, Ghana)

  • Franklin Iron Badzi

    (University of Ghana, Ghana)

  • Michael Agbo Tettey Soli

    (University of Ghana, Ghana)

  • Stephane Jnr Nwolley

    (Npontu Technology, Ghana)

  • Ismail Wafaa Denwar

    (University of Ghana, Ghana)

Abstract

This study aims to analyze the Chan-Vese model's performance using a variety of tumor images. The processes involve the tumors' segmentation, detecting the tumors, identifying the segmented tumor region, and extracting the features before classification occurs. In the findings, the Chan-Vese model performed well with brain and breast tumor segmentation. The model on the skin performed poorly. The brain recorded DSC 0.6949903, Jaccard 0.532558; the time elapsed 7.389940 with an iteration of 100. The breast recorded a DSC of 0.554107, Jaccard 0.383228; the time elapsed 9.577161 with an iteration of 100. According to this study, a higher DSC does not signify a well-segmented image, as the breast had a lower DSC than the skin. The skin recorded a DSC of 0.620420, Jaccard 0.449717; the time elapsed 17.566681 with an iteration of 200.

Suggested Citation

  • Justice Kwame Appati & Franklin Iron Badzi & Michael Agbo Tettey Soli & Stephane Jnr Nwolley & Ismail Wafaa Denwar, 2021. "Validation of Performance Homogeneity of Chan-Vese Model on Selected Tumour Cells," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 12(6), pages 1-17, November.
    • Handle: RePEc:igg:jehmc0:v:12:y:2021:i:6:p:1-17
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