Skripsi

PENGEMBANGAN MODEL SEGMENTASI DAN ALIGMENT CITRA PRA KANKER SERVIKS MENGGUNAKAN ORIENTED BOUNDING BOX DAN YOU ONLY LOOK ONCE

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Penilaian

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Cervical cancer is one of the leading causes of death in women worldwide, so early detection of precancerous lesions is crucial to reduce mortality. Conventional screening methods such as VIA and Pap Smear still have limited accuracy and risk producing false positives and false negatives. This study aims to develop a deep learning-based cervical image segmentation model using the You Only Look Once (YOLO) architecture, YOLOv8 and YOLOv11 versions. In addition, an image alignment approach using Oriented Bounding Box (OBB) was also applied to improve accuracy through rotation based on object orientation. Based on the evaluation results, the YOLOv11m-Seg model with rotation based on the cervical area class showed the best performance with an Intersection over Union (IoU) value of 0.532, a dice coefficient of 0.449, and a pixel accuracy of 93.4%. This model has the potential to support a more accurate and efficient cervical cancer screening system.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507004436	T180095	T1800952025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1800952025

Publisher

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

Collation

xxii, 549 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

-

Subject(s)

Sistem komputer
Prodi Sistem Komputer, Fakultas Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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