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Recognition of Glaucoma by means of Gray Wolf Optimized Neural Network

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Abstract

Glaucoma is the secondary most considerable causes of blindness after Cataracts. As it shows no symptoms, it is entitled to “snake thief of sight”. Glaucoma is the condition in which optic nerve fibers are damaged that causes loss of vision and may even blindness if not diagnosed on time. If this chronic disease is not identified in the initial stage it negatively rooted for permanent blindness. Many manual scanning methods are available however they are costly, time-consuming and require experts of these fields to use them. To circumvent such adversarial effects, it is necessary to spot the disease earlier. This paper proposes a technique for the recognition of Glaucoma called Gray Wolf Optimized Neural Network (GWO-NN), which recognizes the presence or the absence of Glaucoma in a patient. Initially, as a preprocessing phase, the inputted image is transformed to greyscale, noise is eliminated using Adaptive Median Filter (AMF) and image normalization is done. Now, feature extraction is executed in features of GLCM (Gray Level C0-occurrence Matrix features), SURF (Speeded Up Robust Feature), HOG (Histogram of Oriented Gradients features) along with the Global features which are taken from the previously processed image. Now, classification follows utilizing NN of GWO (grey wolf optimization) technique. Experimental outcome indicates that the proposed optimized classifier identifies the existence or nonexistence of Glaucoma more precisely than other existing methods.

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Authors and Affiliations

  1. VV College of Engineering, Tisaiyanvilai, Tamil Nadu, India

    G. Gifta Jerith

  2. Anna University, Chennai, India

    P. Nirmal Kumar

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  1. G. Gifta Jerith
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  2. P. Nirmal Kumar
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Correspondence to G. Gifta Jerith.

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Jerith, G.G., Kumar, P.N. Recognition of Glaucoma by means of Gray Wolf Optimized Neural Network. Multimed Tools Appl 79, 10341–10361 (2020). https://doi.org/10.1007/s11042-019-7224-1

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  • DOI: https://doi.org/10.1007/s11042-019-7224-1

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