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Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Volume 9, 2021 - Issue 6
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Research Article

Automated identification of SD-optical coherence tomography derived macular diseases by combining 3D-block-matching and deep learning techniques

Ilhem Meznia ISTMT, Laboratory of Biophysics and Medical Technologies (LRBTM), LR13ES07, University of Tunis El Manar, Tunis, TunisiaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-6869-1555View further author information
ORCID Icon,
Amine Ben Slamaa ISTMT, Laboratory of Biophysics and Medical Technologies (LRBTM), LR13ES07, University of Tunis El Manar, Tunis, TunisiaORCID Iconhttps://orcid.org/0000-0002-3828-3654View further author information
ORCID Icon,
Zouhair Mbarkib ENSIT, RIFTSI, University of Tunis, Tunis, TunisiaORCID Iconhttps://orcid.org/0000-0003-1799-5182View further author information
ORCID Icon,
Hassen Seddikb ENSIT, RIFTSI, University of Tunis, Tunis, TunisiaView further author information
&
Hedi Trabelsia ISTMT, Laboratory of Biophysics and Medical Technologies (LRBTM), LR13ES07, University of Tunis El Manar, Tunis, TunisiaView further author information
Pages 660-669 | Received 07 Mar 2021, Accepted 03 May 2021, Published online: 09 Jun 2021

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Adel Elamari, Amine Ben Slama, Hedi Trabelsi & Ezeddine Sediki. (2022) Pre-study for facilitating the discovery of microfluidic properties in blood vessels using retinal fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 10:6, pages 599-607.
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Articles from other publishers (1)

Mili Rosline Mathews & Sharafudeen Thaha Mohammed Anzar. (2022) A lightweight deep learning model for retinal optical coherence tomography image classification. International Journal of Imaging Systems and Technology 33:1, pages 204-216.
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