Skripsi

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

---

Penilaian

---

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.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003971	T178370	T1783702025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1783702025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

ix, 136 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

Text

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Sistem komputer

Specific Detail Info

-

Statement of Responsibility

MI

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment