INTERPRETASI OTOMATIS DAN PENINGKATAN KINERJA PADA CITRA MEDIS ULTRASONOGRAFI DENGAN MENGGUNAKAN METODE DEEP LEARNING

SAPITRI, ADE IRIANI and Nurmaini, Siti and Rini, Dian Palupi (2023) INTERPRETASI OTOMATIS DAN PENINGKATAN KINERJA PADA CITRA MEDIS ULTRASONOGRAFI DENGAN MENGGUNAKAN METODE DEEP LEARNING. Doctoral thesis, Sriwijaya University.

	Text RAMA_21001_03013682126014.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (16MB) | Request a copy
	Text RAMA_21001_03013682126014_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (12MB) | Request a copy
	Text RAMA_21001_03013682126014_0002085908_0023027804_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB)
	Text RAMA_21001_03013682126014_0002085908_0023027804_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (123kB) | Request a copy
	Text RAMA_21001_03013682126014_0002085908_0023027804_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_21001_03013682126014_0002085908_0023027804_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_21001_03013682126014_0002085908_0023027804_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (21kB) | Request a copy
	Text RAMA_21001_03013682126014_0002085908_0023027804_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (168kB) | Request a copy
	Text RAMA_21001_03013682126014_0002085908_0023027804_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (12MB) | Request a copy

Abstract

Ultrasonography (USG) is a medical method that uses high-frequency sound waves to produce dynamic visual images in the health sector, especially in prenatal cases. Although ultrasound imaging is a routine examination in medical treatment, medical ultrasound images often have problems such as low resolution, noise, and artifacts. Technological constraints and conventional methods for improving the image quality of medical images open up opportunities for the development of more sophisticated solutions. This study proposes an approach that focuses on the use of advanced technology, especially deep learning, preprocessing, and image enhancement, to improve the image quality of ultrasound medical images. This approach has several main objectives. First, improve the accuracy of diagnosis, time, and cost efficiency in medical practice. Second, overcoming complex image quality problems in medical images through deep learning, image enhancement, and preprocessing technologies. Third, reducing human involvement in the interpretation process by utilizing the proposed technology. This research method will combine the results of preprocessing and image enhancement on various medical image datasets. Deep learning technology will be used to measure improvements in image quality and diagnostic performance. The results of this study are expected to make a significant contribution to the development of medical technology by increasing diagnostic accuracy, efficiency, and productivity in clinics and hospitals. Overall, this study underscores the importance of the image quality of medical ultrasound images, the challenges faced in enhancing them, and the potential of proposed solutions using advanced technologies such as deep learning, preprocessing, and image enhancement. With this approach, it is expected that accuracy and efficiency in patient diagnosis can be significantly improved.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	MEDIS ULTRASONOGRAFI, METODE DEEP LEARNING
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	03-Faculty of Engineering > 21001-Engineering Science (S3)
Depositing User:	Ade Iriani Sapitri
Date Deposited:	18 Jan 2024 07:21
Last Modified:	18 Jan 2024 07:21
URI:	http://repository.unsri.ac.id/id/eprint/138566

Actions (login required)

View Item