Automated Differentiation of Oral Red-White Lesions: An Interpretable Deep Learning Approach Combining Ensemble Architectures and Saliency Maps.

Similarly, when compared against the top‐performing single architecture (Xception), the ensemble maintained a notable, steady edge ( χ 2 = 3.12, p = 0.077), showing a strong trend toward superior diagnostic accuracy even within a highly constrained sample volume. 3.2.

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