SEGMENTASI THORAX JANTUNG JANIN DENGAN MENGGUNAKAN YOU ONLY LOOK ONCE

ALYATISA, ALYATISA and Nurmaini, Siti (2025) SEGMENTASI THORAX JANTUNG JANIN DENGAN MENGGUNAKAN YOU ONLY LOOK ONCE. Undergraduate thesis, Sriwijaya University.

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Abstract

Early detection of fetal heart anomalies, such as cardiomegaly, is critical in prenatal diagnosis. However, manual segmentation of ultrasound (USG) images requires specialized expertise and is time-consuming, highlighting the need for an efficient automated solution. This research aims to perform automatic segmentation of fetal thorax and heart areas using You Only Look Once (YOLO) version 8 and 11 models, with Nano, Small, Medium, and Large variants. The dataset comprises 762 annotated fetal thorax ultrasound images. Model performance was evaluated using mAP (mean Average Precision) and IoU (Intersection over Union) metrics on validation and unseen data. The results indicate that YOLOv11 outperforms YOLOv8, with optimal configurations as follows: YOLOv11 Nano (epoch 200, mAP@50 = 99.1%, IoU = 85.9%), YOLOv11 Small (epoch 100, mAP@50 = 98.6%, IoU = 85.5%), YOLOv11 Medium (epoch 200, mAP@50 = 98.9%, IoU = 86.1%), and YOLOv11 Large (epoch 100, mAP@50 = 99.5%, IoU = 86.5%). These results suggest that YOLOv11 models are effective and reliable for segmenting thorax and heart regions in fetal ultrasound imagery.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	YOLOv11, Segmentasi Jantung Janin, Ultrasonografi, MAP, IOU, Deep Learning.
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:	Alyatisa Alyatisa
Date Deposited:	16 Jul 2025 02:02
Last Modified:	16 Jul 2025 02:02
URI:	http://repository.unsri.ac.id/id/eprint/178507

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