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Despite its robustness, precise tumour segmentation persists as a challenge because of tumour heterogeneity, boundary ambiguity, and the partial volume effects exhibited by tumours. Therefore, the U\u2010Net architecture has been altered many times to expand its capabilities with complex segmentation challenges, particularly with tumours. Following the Preferred Reporting Items for Systematic Reviews and Meta\u2010Analyses (PRISMA) methodology, this systematic review critically evaluates and analyses the effectiveness of recent enhancement strategies developed to optimize the performance of the traditional U\u2010Net architecture in attaining accurate tumour segmentation in CT and MRI images. 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