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Open AccessArticle

A Teleophthalmology Support System Based on the Visibility of Retinal Elements Using the CNNs

1
Graduate Section, Instituto Politécnico Nacional, Mexico City 04440, Mexico
2
Hospital Dr. Luis Sánchez-Bulnes, Asociación para Evitar la Ceguera in México, Mexico City 04030, Mexico
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(10), 2838; https://doi.org/10.3390/s20102838
Received: 8 March 2020 / Revised: 12 May 2020 / Accepted: 13 May 2020 / Published: 16 May 2020
(This article belongs to the Section Intelligent Sensors)
This paper proposes a teleophthalmology support system in which we use algorithms of object detection and semantic segmentation, such as faster region-based CNN (FR-CNN) and SegNet, based on several CNN architectures such as: Vgg16, MobileNet, AlexNet, etc. These are used to segment and analyze the principal anatomical elements, such as optic disc (OD), region of interest (ROI) composed by the macular region, real retinal region, and vessels. Unlike the conventional retinal image quality assessment system, the proposed system provides some possible reasons about the low-quality image to support the operator of an ophthalmoscope and patient to acquire and transmit a better-quality image to central eye hospital for its diagnosis. The proposed system consists of four steps: OD detection, OD quality analysis, obstruction detection of the region of interest (ROI), and vessel segmentation. For the OD detection, artefacts and vessel segmentation, the FR-CNN and SegNet are used, while for the OD quality analysis, we use transfer learning. The proposed system provides accuracies of 0.93 for the OD detection, 0.86 for OD image quality, 1.0 for artefact detection, and 0.98 for vessel segmentation. As the global performance metric, the kappa-based agreement score between ophthalmologist and the proposed system is calculated, which is higher than the score between ophthalmologist and general practitioner. View Full-Text
Keywords: teleophthalmology; support system; quality assessment; deep learning; scanning laser ophthalmoscopes; convolutional neural networks; segmentation teleophthalmology; support system; quality assessment; deep learning; scanning laser ophthalmoscopes; convolutional neural networks; segmentation
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Calderon-Auza, G.; Carrillo-Gomez, C.; Nakano, M.; Toscano-Medina, K.; Perez-Meana, H.; Gonzalez-H. Leon, A.; Quiroz-Mercado, H. A Teleophthalmology Support System Based on the Visibility of Retinal Elements Using the CNNs. Sensors 2020, 20, 2838.

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