IMPLEMENTASI ARSITEKTUR DENSEG-NET DALAM SEGMENTASI SEMANTIK OPTIC DISC DAN OPTIC CUP PADA CITRA RETINA

SAVIERA, MUTIARA and Desiani, Anita and Irmeilyana, Irmeilyana (2022) IMPLEMENTASI ARSITEKTUR DENSEG-NET DALAM SEGMENTASI SEMANTIK OPTIC DISC DAN OPTIC CUP PADA CITRA RETINA. Undergraduate thesis, Sriwijaya University.

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Abstract

Object detection for glaucoma diagnosis can be implemented with images semantic segmentation using Convolutional Neural Network (CNN). Advantage of CNN is able to identify objects in digital images with high accuracy level. One of architecture which used to use in images semantic segmentation is SegNet. This study did the implementation of DenSeg-Net architecture which is modification of SegNet and DenseNet for optic disc and optic cup segmentation in retinal images. The stages of this study are data collecting, data preprocessing, training, testing, evaluation, result analysis, and conclusion. Results of this study using Messidor-2 dataset is score of accuracy, Intersection over Union (IoU), F1-Score, sensitivity, specificity 99.81%, 72.38%, 82.24%, 79.56%, and 96.4% respectively. Based on the results, it can be shown that DenSeg-Net architecture is capable in segmenting optic disc and optic cup from the given images data.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Matematika, Segmentasi, Deep Learning
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	MUTIARA SAVIERA
Date Deposited:	08 Feb 2022 02:09
Last Modified:	01 Apr 2022 04:09
URI:	http://repository.unsri.ac.id/id/eprint/64355

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