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September 10, 2025International Journal of 3D Printing Technologies and Digital IndustryOpen Access

Dimensionality Reduction in Smallpox Histopathological Images Using Autoencoder and Kernel Pca

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Authors

NŞNilgün ŞengözEVEmine Vargün

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Overview

Observational analysis reveals enhanced variance explanation in smallpox images using autoencoder and kernel PCA, suggesting improved diagnostic tools.

Key Points

  • The non-linear autoencoder captured 85.19% variance in the histopathological data, enhancing diagnostic power.
  • Kernel PCA variants, particularly with RBF kernel, explained up to 88.81% of the variance, surpassing other methods.
  • Data preprocessing and model training were crucial for effectively reducing dimensions in complex smallpox images.
  • Findings point to potential advancements in early identification and diagnosis of viral infections using reduced representations.

Cite This Study

Şengöz et al. (2025) studied this question.

synapsesocial.com/papers/68c182589b7b07f3a060f0e0https://doi.org/10.46519/ij3dptdi.1708402
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