Artificial Neural Networks

Czischek, Stefanie

doi:10.1007/978-3-030-52715-0_3

Cited by 1 publication

(1 citation statement)

References 88 publications

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“…An artificial neural network generally consists of three layers, namely: the input layer, namely the nodes that receive the input signal, the middle layer which is also known as the hidden layer, which is the node that connects the nodes in the input layer with the nodes in the output layer and the output layer, namely nodes-the node that generates the output signal. Artificial neural networks learn by adjusting the weight values used to transmit values from one neuron to another [32][33][34][35][36]. Neural networks are one of the most advanced programming methods ever invented.…”

Section: Figure 27: Gray Level Co-occurrence Matrix (Glcm) Methodsmentioning

confidence: 99%

Retinal Analysis and Calculating Cup to Disc Ratio Value Using Deep Learning Models

Nayak¹

2022

IJRASET

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Glaucoma is a disease of the retina caused by high intraocular pressure. The intraocular pressure in people with glaucoma can reach 60-70 mm Hg. This disease is characterized by an increasing cup to disc ratio size. Glaucoma has three levels, namely mild with a cup to disc ratio value of 0.3-0.5, moderate with a cup to disc ratio value of 0.5-0.7 and severe with a cup to disc ratio value above 0.7. For retinal analysis and calculating the cup to disc ratio value taken from a fundus camera, it must be done by an expert ophthalmologist, but it takes a long time. Therefore, feature detection and automatic cup to disc ratio value calculation are expected to assist doctors in analyzing glaucoma. The data used were 132 retinal fundus images consisting of 66 mild glaucoma images, 26 moderate glaucoma images and 40 severe glaucoma images taken from the RIM-ONE dataset (http://medimrg.webs.ull.es). Pre-processing techniques like cropping, resizing, brightness, Median Filter are used for noise removal. Subsequently, feature extraction with the help of GLCM. Consequently, the method used to classify the degree of glaucoma is the Deep Belief Network. The test simulation results obtained accuracy value of 99% with 99% of precision and

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Section: Figure 27: Gray Level Co-occurrence Matrix (Glcm) Methodsmentioning

confidence: 99%

Retinal Analysis and Calculating Cup to Disc Ratio Value Using Deep Learning Models

Nayak¹

2022

IJRASET

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show abstract

Artificial Neural Networks

Cited by 1 publication

References 88 publications

Retinal Analysis and Calculating Cup to Disc Ratio Value Using Deep Learning Models

Retinal Analysis and Calculating Cup to Disc Ratio Value Using Deep Learning Models

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