DETEKSI JANTUNG ANAK MENGGUNAKAN DEEP LEARNING

QODRI, MUHAMMAD HADYAN and Nurmaini, Siti (2023) DETEKSI JANTUNG ANAK MENGGUNAKAN DEEP LEARNING. Undergraduate thesis, Sriwijaya Univeristy.

	Text RAMA_56201_09011281924047.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (5MB) | Request a copy
	Text RAMA_56201_09011281924047_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (16MB) | Request a copy
	Text RAMA_56201_09011281924047_0002085908_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (608kB)
	Text RAMA_56201_09011281924047_0002085908_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (244kB) | Request a copy
	Text RAMA_56201_09011281924047_0002085908_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (179kB) | Request a copy
	Text RAMA_56201_09011281924047_0002085908_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_56201_09011281924047_0002085908_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6kB) | Request a copy
	Text RAMA_56201_09011281924047_0002085908_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (134kB) | Request a copy
	Text RAMA_56201_09011281924047_0002085908_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (236kB) | Request a copy

Abstract

USG or Ultrasonography is a non-invasive medical imaging method that uses high-frequency sound waves to visualize organs, tissues, and structures in the human body, including the hearts of children. This research involves a learning process using deep learning with the Convolutional Neural Network (CNN) architecture called You Only Look Once (YOLO). A total of 11 models from YOLOv7 and YOLOv8 will be used in this research experiment. The data used consists of images of children's hearts obtained from USG examinations, which will be detected using the 11 models. The dataset contains four types of heart conditions: Normal, ASD, AVSD, and VSD. Additionally, there are eight classes of cardiac chambers to be detected in this research: Right Ventricle (RV), Aorta (A), Left Atrium (LA), Left Ventricle (LV), Hole (H), Right Atrium (RA), Pulmonary Atresia (PA), and Right Ventricular Outflow Tract (RVOT). The focus of this research is on the Hole (H) class, as it distinguishes between different cases in the dataset. The parameters used in this study include the number of Epochs, Learning Rate, and Batch Size. The final results of the research include the evaluation of the training process and the testing of unseen data, which will generate outputs such as Mean Average Precision (mAP), F1 Score, Precision, Recall, Confusion Matrix, and predictions from each tested model. Among the 11 models trained and tested using unseen data, the best-performing model is yolov8x, which achieved a total mAP of 96.9% and an accuracy of 83.3% for the Hole (H) class in the training process. In the testing process with unseen data, yolov8x achieved a total mAP of 84.1% and an accuracy of 47.4% for the Hole (H) class. This research is expected to assist in the diagnosis and treatment of cardiac abnormalities in children.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Object Detection, USG, YOLO, Convolutional Neural Network (CNN)
Subjects:	Q Science > Q Science (General) > Q1-390 Science (General) > Q223.M517 Science -- Information services. Information storage and retrieval systems --Science.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Hadyan Qodri
Date Deposited:	02 Aug 2023 06:22
Last Modified:	02 Aug 2023 06:22
URI:	http://repository.unsri.ac.id/id/eprint/122857

Actions (login required)

View Item