SEGMENTASI CITRA RETINA MENGGUNAKAN ARSITEKTUR RESIDUAL NETWORK DAN CONVOLUTIONAL LONG SHORT-TERM MEMORY

SIMAMORA, VALENTINO and Desiani, Anita and Suprihatin, Bambang (2024) SEGMENTASI CITRA RETINA MENGGUNAKAN ARSITEKTUR RESIDUAL NETWORK DAN CONVOLUTIONAL LONG SHORT-TERM MEMORY. Undergraduate thesis, Sriwijaya University.

	Text RAMA_44201_08011282025023.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_44201_08011282025023_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (5MB) | Request a copy
	Text RAMA_44201_08011282025023_0010067102_0006107501_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB)
	Text RAMA_44201_08011282025023_0010067102_0006107501_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (402kB) | Request a copy
	Text RAMA_44201_08011282025023_0010067102_0006107501_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (59kB) | Request a copy
	Text RAMA_44201_08011282025023_0010067102_0006107501_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (438kB) | Request a copy
	Text RAMA_44201_08011282025023_0010067102_0006107501_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (43kB) | Request a copy
	Text RAMA_44201_08011282025023_0010067102_0006107501_06_Ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (195kB) | Request a copy

Abstract

Diabetic Retinopathy is a condition that often causes blindness in the retina. One characteristic of diabetic retinopathy in the retina is the presence of exudates. Exudates segmentation is one way to detect diabetic retinopathy. A commonly used method for exudates segmentation is Convolutional Neural Network (CNN). One architecture found in CNN methods is U-Net.. Even though it is successful in some cases, U-Net sometimes still faces problems such as experiencing overfitting caused by the large number of parameters. To overcome this obstacle, it is necessary to modify the U-Net architecture. This study proposes combining the Residual Network (ResNet) architecture with Convolutional Long Short-Term Memory (ConvLSTM). The encoder part of U-Net is replaced with ResNet to reduce parameters and prevent overfitting. The use of ResNet may result in some features being skipped and not learned. To address this, the decoder part of U-Net is replaced with ConvLSTM so that it can recall the skipped features in the ResNet section. This architecture is capable of producing exudates segmentation with high accuracy. This method has the potential to improve the diagnosis of Diabetic Retinopathy with good segmentation of exudates.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Diabetic Retinopathy, Segmentasi Exudates, ResNet, ConvLSTM, U-Net
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Valentino Simamora
Date Deposited:	30 May 2024 08:02
Last Modified:	30 May 2024 08:02
URI:	http://repository.unsri.ac.id/id/eprint/146111

Actions (login required)

View Item