Skripsi

SEGMENTASI LUBANG DAN SEPTUM JANTUNG PADA ANAK NON-DOPPLER MENGGUNAKAN DEEP LEARNING DENGAN ALGORITMA YOLO

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Penilaian

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Segmentation of heart holes and septum in pediatric medical images presents a major challenge in medical image analysis to support the diagnosis of congenital heart disease. Congenital heart defects such as Atrial Septal Defect (ASD), Ventricular Septal Defect (VSD), Atrioventricular Septal Defect (AVSD), and normal conditions require accurate detection to ensure proper diagnosis. This study employs the YOLOv8 deep learning algorithm to detect and segment heart holes and septum in non-Doppler pediatric heart images. The model is trained with specific configurations to address the complexity of medical images, including variations in object size and location. Evaluation using Intersection over Union (IoU) is conducted to measure the model's accuracy and generalization capability. The results of this research are expected to contribute to the advancement of AI�based technologies in medical applications, particularly in the diagnosis of pediatric heart diseases. Keywords : Heart segmentation, YOLOv8, Deep learning, ASD, VSD, AVSD, Hole, Heart septum, Non-Doppler medical imaging, Congenital heart disease

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507004242	T179289	T1792892025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1792892025

Publisher

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

Collation

xiv, 61 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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