Liver segmentation is detrimental. The toughest segment is the liver. Segment CAD abdominal CT
scans in order to look for liver cancers. The tumor's size, shape, location, and other objects with
comparable intensities in the CT scans make automatic tumor segmentation difficult. Therefore,
precise tumor segmentation was originally made possible by liver segmentation. Since MRI is
essential in medicine, we are concentrating on domain management from MRI to CT volumes,
utilizing 3D and 2D liver segmentation. As a result, we must provide automated liver segmentation
in CT pictures. Utilizing cuckoo optimization, fuzzy c-means, and the random walker's method,
clinical data from patients was segmented. The suggested method was validated using a clinical
liver dataset with one of the highest numbers of tumors for liver tumor segmentation. Zones
impacted by liver illness are separated using fuzzy clustering. Liver boundary data is segmented
using fuzzy C means, fuzzy clustering, and SVM classification. The user may choose a location of
interest and do the contour operations again to increase accuracy with spatial liver boundary
limitations.