PENERAPAN ARSITEKTUR U-NET DAN YOLO V3 DALAM MENDETEKSI OBJEK TRANSVENTRIKULAR PADA CITRA KEPALA JANIN

ALHAFIZ, FAKHRI RAIHAN and Erwin, Erwin (2023) PENERAPAN ARSITEKTUR U-NET DAN YOLO V3 DALAM MENDETEKSI OBJEK TRANSVENTRIKULAR PADA CITRA KEPALA JANIN. Undergraduate thesis, Sriwijaya University.

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Abstract

The content is a research summary about the process of examining the contents of the uterus using Ultrasonography (USG) conducted by doctors. USG is a device that utilizes high-frequency sound waves to produce video images used by experts to identify vital objects within the uterus. The resulting USG images are used for this particular research, which utilizes Convolutional Neural Network technology to detect objects of transventricular fetal head USG. The research's objective is to compare the accuracy of transventricular object detection on fetal heads using the Faster-RCNN architecture from a previous study with the detection method using the YOLOv3 architecture. The segmentation process is performed beforehand to aid in labeling during the detection process. U-Net and YOLOv3 architectures are selected for segmentation and detection processes. Among the 20 models created, Model 11 yields the best results with 92.1% accuracy, and when validated with unseen data, it achieves 88.1% accuracy. The conclusion drawn from the study is that the YOLOv3 architecture outperforms the Faster-RCNN architecture, achieving a final accuracy of 92.1%, compared to Faster-RCNN's 65%

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Deteksi, Segmentasi, Transventrikular, U-NET, YOLO, USG, Convolutional Neural Network (CNN)
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning 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:	Fakhri Raihan Alhafiz
Date Deposited:	23 Aug 2023 03:45
Last Modified:	23 Aug 2023 03:45
URI:	http://repository.unsri.ac.id/id/eprint/127738

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