According　to　the　most　recent　estimates　from　global　cancer　statistics　for　2020,　liver　cancer　is　the　ninth　most　common　cancer　in　women.　Segmenting　the　liver　is　difficult,　and　segmenting　the　tumor　from　the　liver　adds　some　difficulty.　After　a　sample　of　liver　tissue　is　taken,　imaging　tests,　such　as　magnetic　resonance　imaging　(MRI),　computer　tomography　(CT),　and　ultrasound　(US),　are　used　to　segment　the　liver　and　liver　tumor.　Due　to　overlapping　intensity　and　variability　in　the　position　and　shape　of　soft　tissues,　segmentation　of　the　liver　and　tumor　from　computed　abdominal　tomography　images　based　on　shade　gray　or　shapes　is　undesirable.　This　study　proposed　a　more　efficient　method　for　segmenting　liver　and　tumors　from　CT　image　volumes　using　a　hybrid　ResUNet　model,　combining　the　ResNet　and　UNet　models　to　address　this　gap.　The　two　overlapping　models　were　primarily　used　in　this　study　to　segment　the　liver　and　for　region　of　interest　(ROI)　assessment.　Segmentation　of　the　liver　is　done　to　examine　the　liver　with　an　abdominal　CT　image　volume.　The　proposed　model　is　based　on　CT　volume　slices　of　patients　with　liver　tumors　and　evaluated　on　the　public　3D　dataset　IRCADB01.　Based　on　the　experimental　analysis,　the　true　value　accuracy　for　liver　segmentation　was　found　to　be　approximately　99.55%,　97.85%,　and　98.16%.　The　authentication　rate　of　the　dice　coefficient　also　increased,　indicating　that　the　experiment　went　well　and　that　the　model　is　ready　to　use　for　the　detection　of　liver　tumors.