Ocular Phantom-Based Feasibility Study of an Early Diagnosis Device for Glaucoma

Moreno, Marie-Valérie; C, Houriet; Grounauer, P. A.

doi:10.3390/s21020579

Cited by 4 publications

(2 citation statements)

References 21 publications

(25 reference statements)

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“…Glaucoma is a malicious eye disease that harms the eye's optic nerve because of the usual intraocular pressure (IOP) in it [1]. The difference in the produced and drained range of intraocular fluid (IOF) of the eye results in IOP which in turn affects the nerve fibers (NF).…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization

Nawaz

Nazir

Javed

et al. 2022

Sensors

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Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification.

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Section: Introductionmentioning

confidence: 99%

“…In another study conducted in [10], it is forecasted that by 2040, the number of glaucomatous victims will rise to 111.8 million. The extensive increase in the growth rate of glaucoma will introduce a social and financial load on the world economy and have an impact on the comfort of victims [1].…”

Section: Introductionmentioning

confidence: 99%