Text

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

---

Penilaian

---

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.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2207000866	T64355	T643552022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T643552022

Publisher

Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2022

Collation

xii, 63 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

519.507

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Jurusan Matematika
Matematika Statistikal

Specific Detail Info

-

Statement of Responsibility

SEPTA

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment