Mimetic Finite Difference Methods for Restoration of Fundus Images for Automatic Glaucoma Detection

Bautista, Lola; Villamizar, Jorge; Calderón, Giovanni; Carrillo, Claudia; Rueda, J. A.; Castillo, José E.

doi:10.1007/978-3-030-32040-9_12

Cited by 2 publications

(1 citation statement)

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“…It is the ratio of the size of the optic nerve (named as a disc) to the size of the excavation that occurs inside the optic nerve during the enlargement of eye pressure (named cup). This parameter is known as the Cup-to-Disc Ratio (CDR) [10]. Several studies have been focusing on automated identification of glaucoma based on color fundus images, but the CDR estimate is the most complex one.…”

Section: Introductionmentioning

confidence: 99%

FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

Madhumalini

Devi

2021

Preprint

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Glaucoma is a retinal disease that damages the eye's optic nerve, frequently causing an irreversible loss of vision. However, the accurate diagnosis of this disease is difficult but early-stage diagnosis may cure this retinal disease. The objective of this research is to diagnose glaucoma disease in the top of the eye's optical nerve. The proposed approach detects glaucoma via four major steps namely Data enhancement phase, segmentation phase, feature extraction phase, and classification phase by the fractional gravitational search-based hybrid deep neural network (FGSA-HDNN) classifier. The proposed classifier is used for the exact classification of glaucoma infected images and normal images. Here, the proposed approach utilizes the statistical, textural, and vessel features from the segmented output. Also, the proposed FGSO algorithm is used for testing the deep neural network. From the experimental results, it is observed that the proposed glaucoma detection has obtained a sensitivity of 99.64%, a specificity of 97.84%, and an accuracy of 98.75% that outperforms other state-of-art methods.

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Section: Introductionmentioning

confidence: 99%