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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
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SSDA_AOA: Stacked Sparse Denoising Autoencoder With Archimedes Optimization Algorithm Based Oral Cancer Detection on

R Sathish Kumar1, M Govindarajan1

  • 1Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India.

Oral Diseases
|February 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computer-aided diagnostic framework for oral cancer detection, achieving 95.38% accuracy. The deep learning and optimization approach enhances early diagnosis and improves patient outcomes.

Keywords:
Archimedes optimization algorithmNASNet‐Largehistopathological imagesoral cancerstacked sparse denoising autoencoder

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Area of Science:

  • Oncology
  • Computer Science
  • Medical Imaging

Background:

  • Oral cancer diagnosis presents significant challenges, impacting patient outcomes.
  • Early detection is crucial for effective treatment and improved survival rates.
  • Current diagnostic tools require enhancement for greater accuracy and efficiency.

Purpose of the Study:

  • To develop a novel computer-aided diagnostic (CAD) framework for oral cancer.
  • To leverage deep learning (DL) and optimization techniques for enhanced early detection.
  • To improve diagnostic accuracy and patient outcomes in oral cancer cases.

Main Methods:

  • Histopathological images were preprocessed and analyzed using the NASNet-Large model for feature extraction.
  • The Archimedes Optimization Algorithm (AOA) performed simultaneous dimensionality reduction and hyperparameter tuning.
  • A Stacked Sparse Denoising Autoencoder (SSDA) classifier was employed for robust latent representation learning.

Main Results:

  • The proposed SSDA-AOA framework achieved high performance metrics.
  • Accuracy: 95.38%
  • Precision: 95.15%, Sensitivity: 91.78%, Specificity: 91.85%, F1-Score: 93.72%.

Conclusions:

  • The SSDA-AOA framework shows significant potential for early and precise oral cancer detection.
  • This approach can facilitate timely intervention, leading to improved patient outcomes.
  • The study highlights the need for advanced computational tools in oncology diagnostics.