<p>Colorectal cancer is a major health concern, ranking as one of the most common and deadly forms of cancer. It typically begins as polyps, which are abnormal growths in the intestinal mucosa. Identifying and removing these polyps through colonoscopy is a crucial preventative measure. However, even experienced professionals can overlook some polyps during examinations. In this context, segmentation algorithms can assist medical professionals by identifying areas in an image that correspond to a polyp. These algorithms, which rely on deep learning, require extensive image datasets to effectively learn how to identify and segment polyps. This study aimed to identify public colonoscopy image datasets that contain polyps and to examine how combining these datasets might affect the performance of deep learning-based segmentation algorithms. After selecting the datasets and defining their combinations, we trained nine segmentation algorithms on each combination. The evaluation of the trained models showed that merging datasets can enhance model generalization, with increases of up to 0.367 in both the dice coefficient and in the Intersection over Union (IoU) metrics. These improvements could lead to higher diagnostic accuracy in clinical settings, enhancing efforts to prevent colorectal cancer.</p>