SEGMENTASI LESI PRA-KANKER SERVIKS MENGGUNAKAN YOLO DAN SEGMENT ANYTHING MODEL

ROLIS, MUHAMMAD ALPIM ALFA and Nurmaini, Siti (2025) SEGMENTASI LESI PRA-KANKER SERVIKS MENGGUNAKAN YOLO DAN SEGMENT ANYTHING MODEL. Undergraduate thesis, Sriwijaya University.

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Abstract

With the advancement of technology, deep learning models are increasingly being used in the medical field, particularly in image segmentation. This study aims to develop and evaluate methods for segmenting pre-cancerous cervical lesions using the YOLO (You Only Look Once) architecture, specifically YOLOv8 and YOLOv11, as well as the Segment Anything Model (SAM and SAM 2). The YOLO model is used to detect the cervical area, columnar area (CA), and lesions in medical images obtained from Mohammad Hoesin General Hospital and the International Agency for Research on Cancer (IARC). The bounding box detection results from YOLO are used as prompts for the segmentation process using SAM, forming a modular pipeline that enables optimization. The research results indicate that the YOLO+SAM approach provides competitive segmentation performance and modular flexibility, supporting an automated segmentation system for pre-cancerous cervical lesions for early diagnosis processes.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Image Segementation, Deep Learning, Segment Anything Model, YOLO
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Alpim Alfa Rolis
Date Deposited:	14 Jul 2025 06:55
Last Modified:	14 Jul 2025 06:55
URI:	http://repository.unsri.ac.id/id/eprint/178370

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