Deep learning and classic machine learning models in the automatic diagnosis of multiple sclerosis using retinal vessels-Reference-Cited by-同舟云学术

Deep learning and classic machine learning models in the automatic diagnosis of multiple sclerosis using retinal vessels

Published:2023-09-30 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Yaghoubi Neda^ORCID,Masumi Hassan,Fatehi Mohammad Hossein^ORCID,Ashtari Fereshteh^ORCID,Kafieh Rahele^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-16812-w.pdf

Reference45 articles.

1. Bhaduri B, Nolan RM, Shelton RL, Pilutti LA, Motl RW, Moss HE, ... Boppart SA (2016) Detection of retinal blood vessel changes in multiple sclerosis with optical coherence tomography. Biomed Opt Exp 7(6):2321–2330

2. Wang X, Jia Y, Spain R, Potsaid B, Liu JJ, Baumann B, ... Huang D (2014) Optical coherence tomography angiography of optic nerve head and parafovea in multiple sclerosis. Br J Ophthalmol 98(10):1368–1373

3. Tyler C (2016) A novel retinal blood vessel segmentation algorithm for fundus images. MATLAB Cent. File Exch.

4. Ridler TW, Calvard S (1978) Picture thresholding using an iterative selection method. IEEE Transactions on Systems, Man, and Cybernetics SMC-8, 630–632

5. Setiawan W, Damayanti F (2016) Fundus image classification using two dimensional linear discriminant analysis and support vector machine. Int J Adv Eng Manag Sci 2(10):1–6

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

2. Utilizing Long Short-Term Memory for Detecting Multiple Sclerosis Based on Vessel Analysis;International Journal of Optics and Photonics;2023-01-01