Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis

Naim Rochmawati; Chastine Fatichah; Bilqis Amaliah; Agus Budi Raharjo; Frédéric Dumont; Emilie Thibaudeau; Cédric Dumas

doi:10.1007/978-3-031-70906-7_9

Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis

Naim Rochmawati^*
, Chastine Fatichah
, Bilqis Amaliah
, Agus Budi Raharjo
, Frédéric Dumont
, Emilie Thibaudeau
, Cédric Dumas

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember
Institut de Cancérologie de l'Ouest
Institut Mines-Télécom Atlantique

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	Advances in Computing and Data Sciences - 8th International Conference, ICACDS 2024, Revised Selected Papers
Editors	Mayank Singh, Vipin Tyagi, P.K. Gupta, Jan Flusser, Tuncer Ören, Amar Ramdane Cherif, Ravi Tomar
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	93-104
Number of pages	12
ISBN (Print)	9783031709050
DOIs	https://doi.org/10.1007/978-3-031-70906-7_9
Publication status	Published - 2025
Event	8th International Conference on Advances in Computing and Data Sciences, ICACDS 2024 - Velizy, France Duration: 9 May 2024 → 10 May 2024

Publication series

Name	Communications in Computer and Information Science
Volume	2194 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	8th International Conference on Advances in Computing and Data Sciences, ICACDS 2024
Country/Territory	France
City	Velizy
Period	9/05/24 → 10/05/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

YOLO
comparison
object detection
peritoneal carcinomatosis

Access to Document

10.1007/978-3-031-70906-7_9

Cite this

Rochmawati, N., Fatichah, C., Amaliah, B., Raharjo, A. B., Dumont, F., Thibaudeau, E., & Dumas, C. (2025). Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis. In M. Singh, V. Tyagi, P. K. Gupta, J. Flusser, T. Ören, A. R. Cherif, & R. Tomar (Eds.), Advances in Computing and Data Sciences - 8th International Conference, ICACDS 2024, Revised Selected Papers (pp. 93-104). (Communications in Computer and Information Science; Vol. 2194 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-70906-7_9

Rochmawati, Naim ; Fatichah, Chastine ; Amaliah, Bilqis et al. / Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis. Advances in Computing and Data Sciences - 8th International Conference, ICACDS 2024, Revised Selected Papers. editor / Mayank Singh ; Vipin Tyagi ; P.K. Gupta ; Jan Flusser ; Tuncer Ören ; Amar Ramdane Cherif ; Ravi Tomar. Springer Science and Business Media Deutschland GmbH, 2025. pp. 93-104 (Communications in Computer and Information Science).

@inproceedings{1274235a42bd4026927969a9b1a50e59,

title = "Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis",

abstract = "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.",

keywords = "YOLO, comparison, object detection, peritoneal carcinomatosis",

author = "Naim Rochmawati and Chastine Fatichah and Bilqis Amaliah and Raharjo, \{Agus Budi\} and Fr{\'e}d{\'e}ric Dumont and Emilie Thibaudeau and C{\'e}dric Dumas",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 8th International Conference on Advances in Computing and Data Sciences, ICACDS 2024 ; Conference date: 09-05-2024 Through 10-05-2024",

year = "2025",

doi = "10.1007/978-3-031-70906-7\_9",

language = "English",

isbn = "9783031709050",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "93--104",

editor = "Mayank Singh and Vipin Tyagi and P.K. Gupta and Jan Flusser and Tuncer {\"O}ren and Cherif, \{Amar Ramdane\} and Ravi Tomar",

booktitle = "Advances in Computing and Data Sciences - 8th International Conference, ICACDS 2024, Revised Selected Papers",

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Rochmawati, N, Fatichah, C , Amaliah, B , Raharjo, AB, Dumont, F, Thibaudeau, E & Dumas, C 2025, Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis. in M Singh, V Tyagi, PK Gupta, J Flusser, T Ören, AR Cherif & R Tomar (eds), Advances in Computing and Data Sciences - 8th International Conference, ICACDS 2024, Revised Selected Papers. Communications in Computer and Information Science, vol. 2194 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 93-104, 8th International Conference on Advances in Computing and Data Sciences, ICACDS 2024, Velizy, France, 9/05/24. https://doi.org/10.1007/978-3-031-70906-7_9

Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis. / Rochmawati, Naim; Fatichah, Chastine ; Amaliah, Bilqis et al.
Advances in Computing and Data Sciences - 8th International Conference, ICACDS 2024, Revised Selected Papers. ed. / Mayank Singh; Vipin Tyagi; P.K. Gupta; Jan Flusser; Tuncer Ören; Amar Ramdane Cherif; Ravi Tomar. Springer Science and Business Media Deutschland GmbH, 2025. p. 93-104 (Communications in Computer and Information Science; Vol. 2194 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 - https://www.scopus.com/pages/publications/85208024687

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 -

Rochmawati N, Fatichah C , Amaliah B , Raharjo AB, Dumont F, Thibaudeau E et al. Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis. In Singh M, Tyagi V, Gupta PK, Flusser J, Ören T, Cherif AR, Tomar R, editors, Advances in Computing and Data Sciences - 8th International Conference, ICACDS 2024, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2025. p. 93-104. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-70906-7_9

Comparative Analysis of YOLO-Based Object Detection Models for Peritoneal Carcinomatosis

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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