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The classification stage consists of two sub-steps, where the first is carried out to the training of a deep learning known as Convolutional Neural Networks (CNN), the second sub-step is the validation of the model, that is, the tests with unknown images by CNN (Saraiva.
Mar 19, 2020 · Classification of optical coherence tomography (OCT) images can be achieved with high accuracy using classical convolution neural networks ...
Modern diagnosis systems use Convolutional Neural Networks. Our model increases the contrast in the residual connection, so high contrast regions, such as the ...
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Abstract: This article describes a classification model of optical coherence tomography images using convolution neural network.
Mar 19, 2020 · Results: Classification of OCT images using our method achieved accuracy of 99.6%, which is 3.2 percentage points higher than that of other ...
This article describes a classification model of optical coherence tomography images using convolution neural network that was shown to be efficient, ...
In this paper, a novel architecture based on Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) is proposed to classify OCT images.
We will discuss classification method to classify retinal OCT images automatically based on convolutional neural networks (CNN's).
Our study demonstrates that a deep learning neural network was effective at distinguishing AMD from normal OCT images, and its accuracy was even higher when ...
Sep 5, 2023 · Regarding OCT images classification, the most used CNN architectures are VGG, ResNet and Inception, and have shown very promising results so far ...