Skripsi

SEGMENTASI PEMBULUH DARAH PADA CITRA RETINA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK VV-NET

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Penilaian

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The U-Net architecture has a fairly deep network. The addition of layers in the U-Net architecture network can increase the complexity of the U-Net network which can affect the training time to be longer and parameter enlargement. This study modifies U-Net by reducing the complexity of U-Net by removing the bridge part of U-Net. The removal of the bridge in U-Net is known as V-Net architecture. The removal of the bridge has the risk of underfitting. To avoid the risk of underfitting, a modification of V-Net is proposed by performing V-Net twice for blood vessel segmentation. The application of V-Net twice is referred to as VV-Net architecture. The first V-Net is used for feature extraction and the second V-Net is used to improve feature extraction so as to produce better segmentation. This study aims to determine the performance evaluation results of the VV-Net architecture. The evaluation measures used are accuracy, sensitivity, precision and Jaccard score. Tests were conducted on the DRIVE, STARE, and CHASEDB_1 datasets. The measurement results of blood vessel segmentation using VV-net on the DRIVE dataset resulted in accuracy 96.27%, sensitivity 84.38%, precision 75.95%, and Jaccard score 66.28%. On the STARE dataset, the accuracy result is 96.58%, sensitivity 82.78%, precission 76.73%, and Jaccard score 65.38%. Meanwhile, the CHASEDB_1 dataset resulted in 97.04% accuracy, 83.55% sensitivity, 76.72% precission, and 66.40% Jaccard score. Based on these results, it shows that the proposed VV-Net architecture is very good in segmenting blood vessels, indicated by accuracy values above 90%, sensitivity above 80%, and precision above 70%. The Jaccard score value is still below 70%, indicating that the proposed architecture is quite good at detecting faint blood vessel regions. Since the Jaccard score value is still below 70%, the focus of further research is to make improvements to the proposed architecture to increase the Jaccard score value.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407003132	T145161	T1451612024	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1451612024

Publisher

Indralaya : Prodi Magister Ilmu Komputer, Fakultas Ilmu Komputer., 2024

Collation

xv, 70 hlm.; ilus.; tab.; 28 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

unspecified

Edition

-

Subject(s)

Prodi Magister Ilmu Komputer
Convolutional Neural Network

Specific Detail Info

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Statement of Responsibility

UIN Farrah

Other version/related

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