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💡[Feature]: Pseudo Papilledema Detection using Deep Learning #324
Deep learning techniques are leveraged in pseudo-papilledema detection by training convolutional neural networks (CNNs) on large datasets of retinal images to differentiate between true papilledema and pseudo-papilledema.
These models can automatically extract relevant features from the images, leading to high accuracy in identifying subtle differences that might be challenging for human observers.
This approach improves diagnostic accuracy, reduces the need for invasive procedures, and supports ophthalmologists in making timely and precise decisions.
@TAHIR0110, @Avdhesh-Varshney, could you please assign me this issue under GSSOC'24
Use Case
Supports ophthalmologists in making timely and precise decisions.
Is there an existing issue for this?
Feature Description
@TAHIR0110, @Avdhesh-Varshney, could you please assign me this issue under GSSOC'24
Use Case
Supports ophthalmologists in making timely and precise decisions.
Benefits
No response
Add ScreenShots
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Priority
High
Record