VG-DropDNet a Robust Architecture for Blood Vessels Segmentation on Retinal Image

Author:

Desiani Anita1,Erwin 2ORCID,Suprihatin Bambang1,Efriliyanti Filda3,Arhami Muhammad4,Setyaningsih Emy5

Affiliation:

1. Mathematics Department, Mathematics and Natural Science Faculty, Universitas Sriwijaya, Indralaya, Indonesia

2. Computer Engineering Department, Computer Science Faculty, Universitas Sriwijaya, Indralaya, Indonesia

3. Computation Laboratory, Mathematics and Natural Science Faculty, Universitas Sriwijaya, Indralaya, Indonesia

4. Informatics Engineering Department, Politeknik Negeri Lhokseumawe, Lhokseumawe, Indonesia

5. Computer Systems Department, Institut Sains dan Teknologi Akprind, Yogyakarta, Indonesia

Funder

Computation Laboratory, Mathematic and Natural Science Faculty, Universitas Sriwijaya

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

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1. Improved U-Net Performance with Augmentation for Retinal Optic Segmentation;2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS);2023-11-07

2. Back Translation-EDA and Transformer for Hate Speech Classification in Indonesian;2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS);2023-11-07

3. Utilizing Long Short-Term Memory (LSTM) for Detecting Multiple Sclerosis Based on Vessel Analysis;2023-10-30

4. ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION;Applied Computer Science;2023-09-30

5. A retinal vessel segmentation method based improved U-Net model;Biomedical Signal Processing and Control;2023-04

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