Colorectal cancer has a high incidence and mortality rate in Taiwan. Clinically, diagnosis is typically performed using colonoscopy and computed tomography (CT). Compared with colonoscopy, which allows direct visualization and biopsy but often causes significant discomfort that may reduce patient compliance, CT colonography is noninvasive, provides high-resolution images rapidly, and enables visualization of intestinal structures to assist in tumor localization. This retrospective study collected data from 237 patients. CT images were annotated for colon regions and tumor locations. Segmentation models based on nnU-Net and YOLO were developed to delineate the colon and colorectal tumors. To enhance tumor segmentation accuracy, the initially segmented colon region was used as a mask to exclude non-colonic areas, enabling more precise tumor delineation within the intestine. Model performance was evaluated using the Dice Similarity Coefficient (DSC) and Intersection over Union (IoU). The best performing model achieved DSC and IoU values of 91.1% and 85.7% for colon segmentation, and 78.0% and 69.8% for tumor segmentation, respectively. Results indicate that nnU-Net and YOLO models can effectively perform automated segmentation of the colon and tumors on CT images, with excellent accuracy in colon segmentation. Although tumor morphology and boundaries are diverse, the proposed approach demonstrates robust recognition capability. Furthermore, applying colon masks to assist tumor segmentation significantly improves localization precision and reduces misclassification. This strategy holds promise for integration into clinical decision-support systems to enhance early colorectal cancer detection and treatment outcomes.