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

ADITIAWATI, ADITIAWATI and Nurmaini, Siti (2025) SEGMENTASI LUBANG DAN SEPTUM JANTUNG PADA ANAK NON-DOPPLER MENGGUNAKAN DEEP LEARNING DENGAN ALGORITMA YOLO. Undergraduate thesis, Sriwijaya University.

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Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Segmentasi jantung, YOLOv8, Deep learning, ASD, VSD, AVSD, Hole, Septum jantung, Citra medis Non-Doppler, Penyakit jantung bawaan.
Subjects:	R Medicine > RJ Pediatrics > RJ50-51 Examination. Diagnosis T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Aditiawati Aditiawati
Date Deposited:	19 Jul 2025 07:09
Last Modified:	19 Jul 2025 07:09
URI:	http://repository.unsri.ac.id/id/eprint/179289

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