2020
DOI: 10.3390/s20102838
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A Teleophthalmology Support System Based on the Visibility of Retinal Elements Using the CNNs

Abstract: 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… Show more

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Cited by 6 publications
(4 citation statements)
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“…CNNs have been widely used for medical image segmentation, given their success and efficiency, including proposals for ophthalmic scans, such as retinal vessel segmentation and retinal layer segmentations [32,33]. For the segmentation of images of the anterior eye structures, several architectures have been used, such as faster region-based CNN (FR-CNN) [34], PIPE-Net [35], and Lenet-5 [36]. Different networks have been developed for training using smaller numbers of images with improved levels of success.…”
Section: Cnn-based Cornea and Iris Segmentationmentioning
confidence: 99%
“…CNNs have been widely used for medical image segmentation, given their success and efficiency, including proposals for ophthalmic scans, such as retinal vessel segmentation and retinal layer segmentations [32,33]. For the segmentation of images of the anterior eye structures, several architectures have been used, such as faster region-based CNN (FR-CNN) [34], PIPE-Net [35], and Lenet-5 [36]. Different networks have been developed for training using smaller numbers of images with improved levels of success.…”
Section: Cnn-based Cornea and Iris Segmentationmentioning
confidence: 99%
“…The three models are applied (U-Net, VGG16-Segnet, and DeepLabv3+) in the proposed system, since their decoders produce outputs that are of the same dimensions as the input image, which suits the task of segmentation. In addition, they have repeatedly used in similar medical image segmentation tasks, such as skin lesion segmentation [19], Liver lesion segmentation [20], [21], lung segmentation [22], [23], and pathological lymph node segmentation [24].…”
Section: Feature Extractionmentioning
confidence: 99%
“…These techniques offer a wide array of applications that contribute to the overall improvement of healthcare outcomes. Applications such as abnormalities segmentation, 10 quantification of physiological parameters, 11 early disease detection 12 or image quality assessment systems, 13 are some valuable applications. Furthermore, contrast enhancement techniques, 14,15 are employed to improve image quality, enhance visibility of important details, and facilitate more accurate diagnosis.…”
Section: Introductionmentioning
confidence: 99%