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	<title>APTOS 2019 dataset &#8211; BIOENGINEER.ORG</title>
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	<title>APTOS 2019 dataset &#8211; BIOENGINEER.ORG</title>
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		<title>Graph Neural Networks Push Diabetic Retinopathy Detection Toward Near-Perfect Accuracy</title>
		<link>https://bioengineer.org/graph-neural-networks-push-diabetic-retinopathy-detection-toward-near-perfect-accuracy/</link>
		
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		<pubDate>Sun, 11 Oct 2026 03:37:28 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[APTOS 2019 dataset]]></category>
		<category><![CDATA[Computer Vision]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[diabetic retinopathy]]></category>
		<category><![CDATA[Explainable AI]]></category>
		<category><![CDATA[Graph Isomorphism Network]]></category>
		<category><![CDATA[graph neural networks]]></category>
		<category><![CDATA[GraphSAGE]]></category>
		<category><![CDATA[medical image classification]]></category>
		<category><![CDATA[Ophthalmology]]></category>
		<category><![CDATA[ResNet50]]></category>
		<category><![CDATA[retinal images]]></category>
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					<description><![CDATA[Researchers in India have built a hybrid graph neural network framework that converts retinal images into graph structures, achieving up to 96.86 percent accuracy and near-perfect ROC-AUC in detecting diabetic retinopathy.]]></description>
		
		
		
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