Peritoneal carcinomatosis is a malignant cancer that spreads to the surface lining of a person's abdominal cavity and is usually caused by infection from other organs. AI developments, one of which is YOLO, can be used to help detect peritoneal carcinomatosis lesions. This research detects peritoneal carcinomatosis lesions by comparing several versions of YOLO with different scales, namely YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, YOLOv6sn, YOLOv6s, YOLOv6m, YOLOv6l, YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l. Recall, precision and mean Average Precision (mAP) metrics are all used in this study as well as inference time. The results show that the recommended models are YOLOv8l and YOLOv5l where both get the same high results with mAP of 0.799, followed by YOLOv8s, with mAP results of 0.796. The study's findings are intended to direct future clinical applications and determine the most appropriate model for the identification of peritoneal carcinomatosis. This study provides in-depth information that forms the basis for informed decision-making, highlighting the accuracy required to address issues related to peritoneal carcinomatosis.