Appati, J.K.Badzi, F.I.Soli, M.A.T.Nwolley, S.J.Denwar, I.W.2021-11-042021-11-042021DOI: 10.4018/IJEHMC.20211101.oa7http://ugspace.ug.edu.gh/handle/123456789/37017Research ArticleThis 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.enChan-VeseDeformable ModelsDice Similarity CoefficientLevel SetTumorValidation of Performance Homogeneity of Chan-Vese Model on Selected Tumour CellsArticle