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

Justice Kwame Appati, Franklin Iron Badzi, Michael Agbo Tettey Soli, Stephane Jnr Nwolley and Ismail Wafaa Denwar
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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

International Journal of E-Health and Medical Communications (IJEHMC), 2021, vol. 12, issue 6, 1-17

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.

Date: 2021
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