TY - GEN
T1 - Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis
AU - Rochmawati, Naim
AU - Fatichah, Chastine
AU - Amaliah, Bilqis
AU - Raharjo, Agus Budi
AU - Dumont, Frédéric
AU - Thibaudeau, Emilie
AU - Dumas, Cédric
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - YOLO
KW - comparison
KW - object detection
KW - peritoneal carcinomatosis
UR - http://www.scopus.com/inward/record.url?scp=85208024687&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70906-7_9
DO - 10.1007/978-3-031-70906-7_9
M3 - Conference contribution
AN - SCOPUS:85208024687
SN - 9783031709050
T3 - Communications in Computer and Information Science
SP - 93
EP - 104
BT - Advances in Computing and Data Sciences - 8th International Conference, ICACDS 2024, Revised Selected Papers
A2 - Singh, Mayank
A2 - Tyagi, Vipin
A2 - Gupta, P.K.
A2 - Flusser, Jan
A2 - Ören, Tuncer
A2 - Cherif, Amar Ramdane
A2 - Tomar, Ravi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Conference on Advances in Computing and Data Sciences, ICACDS 2024
Y2 - 9 May 2024 through 10 May 2024
ER -
