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		<title>Ensemble of Three CNNs Reads Breast Cancer Slides With 97% Accuracy</title>
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		<pubDate>Fri, 09 Oct 2026 14:12:04 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[BreaKHis]]></category>
		<category><![CDATA[Breast Cancer]]></category>
		<category><![CDATA[CBAM ablation]]></category>
		<category><![CDATA[computer-aided diagnosis]]></category>
		<category><![CDATA[convolutional neural networks]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[DenseNet]]></category>
		<category><![CDATA[Explainable AI]]></category>
		<category><![CDATA[Grad-CAM]]></category>
		<category><![CDATA[histopathology]]></category>
		<category><![CDATA[soft voting ensemble]]></category>
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					<description><![CDATA[Researchers in Algeria built a soft-voting ensemble of three convolutional neural networks that classifies breast cancer histopathology images with 97.12 percent accuracy while showing that attention modules actually hurt performance.]]></description>
		
		
		
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