Features of CNN architectures.

<div><p>Background</p><p>Retinal problems are critical because they can cause severe vision loss if not treated. Traditional methods for diagnosing retinal disorders often rely heavily on manual interpretation of optical coherence tomography (OCT) images, which can be time-co...

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Bibliographic Details
Main Author:	Gülcan Gencer (20691782) (author)
Other Authors:	Kerem Gencer (20691785) (author)
Published:	2025
Subjects:	Medicine Neuroscience Biotechnology Cancer Biological Sciences not elsewhere classified Information Systems not elsewhere classified provide high success produced excellent results optical coherence tomography indicate substantial promise duke &# 8217 aided diagnostic tools adaptively recalibrating per using artificial intelligence image classification tasks se blocks increase model scaling strategies model &# 8217 detecting retinal disorders dme ), drusen oct images using retinal disorders categorized using including dme classification performance proposed se hybrid se xlink "> traditional methods softmax algorithm paper offers oct images oct datasets manual interpretation known approaches findings emphasize fewer parameters early diagnosis early detection current best combined features choroidal neovascularization architectures enhances accurate detection accuracy rates
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Features of CNN architectures.

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