SEGMENTASI PEMBULUH DARAH PADA CITRA RETINA MENGGUNAKAN KOMBINASI ARSITEKTUR U-NET, RESNET, DAN BIDIRECTIONAL CONVOLUTIONAL LONG SHORT TERM MEMORY

PUTRI, AJENG ISLAMIA and Desiani, Anita and Irmeilyana, Irmeilyana (2021) SEGMENTASI PEMBULUH DARAH PADA CITRA RETINA MENGGUNAKAN KOMBINASI ARSITEKTUR U-NET, RESNET, DAN BIDIRECTIONAL CONVOLUTIONAL LONG SHORT TERM MEMORY. Undergraduate thesis, Sriwijaya University.

	Text RAMA_44201_08011181722002.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_44201_08011181722002_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (18MB) | Request a copy
Preview	Text RAMA_44201_08011181722002_0011127702_0017057403_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (942kB) | Preview
	Text RAMA_44201_08011181722002_0011127702_0017057403_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_44201_08011181722002_0011127702_0017057403_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (221kB) | Request a copy
	Text RAMA_44201_08011181722002_0011127702_0017057403_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_44201_08011181722002_0011127702_0017057403_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (87kB) | Request a copy
	Text RAMA_44201_08011181722002_0011127702_0017057403_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (228kB) | Request a copy

Abstract

The addition of layers to the U-Net architecture can cause more parameters and redundant computation. The use of skip connection in the ResNet architecture can solve this problem, but it can cause the loss of important features from the previous layer. Bi-directional Convolutional Long Short Term Memory (Bi-ConvLSTM) can store previous and current information for a long time so that the missing features of the ResNet architecture can be overcome. The purpose of this study was to determine the performance evaluation results of combination architectures of U-Net, ResNet, and Bi-ConvLSTM on retinal blood vessels segmentation. The evaluation measures used were accuracy, sensitivity, specificity, F1-Score, and IoU. The data used in this study is the DRIVE dataset which is divided into 20 training data and 20 test data. The methods used in this study are data collection, data pre-processing, architecture implementation, training, testing, and evaluation. The values of accuracy, sensitivity, specificity, F1-Score, and IoU obtained were 95.56%, 79.24%, 97.22%, 76.69%, and 62.18%, respectively. Based on these results, we can conclude that the proposed architecture has been successfully performed segmentation of retinal blood vessels and predicts background pixels very well, indic ated by the accuracy and specificity values above 90%. In addition, it is well enough in predicting retinal blood vessels and the harmonization between the sensitivity and specificity values indicated by the sensitivity and F1-Score values above 70%, but the similarity between the image segmentation results and ground truth is still not good, which is below 70%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Pembuluh darah retina, Segmentasi, U-Net, ResNet, Bi-ConvLSTM
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science. Q Science > QA Mathematics > QA1-939 Mathematics > QA1.T553 Mathematics--Periodicals. Computer science--Periodicals. Computer science. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Ajeng Islamia Putri
Date Deposited:	19 Oct 2021 06:57
Last Modified:	19 Oct 2021 06:57
URI:	http://repository.unsri.ac.id/id/eprint/56091

Actions (login required)

View Item