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Oral cavity carcinoma detection using BAT algorithm-optimized machine learning models with transfer learning and

Sakinat O Folorunso1, Akinshipo Abdulwarith2, Abidemi Emmanuel Adeniyi3

  • 1Artificial Intelligence Systems Research Group, Department of Computer Science, Olabisi Onabanjo University, Ago-Iwoye, Nigeria.

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|May 6, 2025
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Summary

This study introduces a novel AI framework (TR-ROS-BAT-ML) for improved oral cavity carcinoma detection. The system effectively handles imbalanced data, achieving high diagnostic recall for early lesion identification.

Keywords:
BAT algorithmEnsembleOral cavity carcinomaRandom oversamplingTransfer learning

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Healthcare

Background:

  • Oral cavity carcinoma is a significant public health issue requiring accurate early detection.
  • Existing diagnostic methods struggle with feature selection, imbalanced datasets, and computational efficiency.

Purpose of the Study:

  • To develop a novel diagnostic framework (TR-ROS-BAT-ML) for enhanced oral cavity carcinoma detection.
  • To integrate transfer learning, random oversampling, BAT algorithm optimization, and ensemble machine learning.

Main Methods:

  • Utilized a dataset of 1224 H&E-stained histological images from normal oral epithelium and OSCC.
  • Employed pre-trained deep learning models for feature extraction and random oversampling for class imbalance.
  • Applied the BAT algorithm for feature selection and hyperparameter tuning, followed by ensemble classification.

Main Results:

  • The TR-ROS-BAT-ML framework demonstrated high diagnostic performance, with the optimized Extra Trees (ET) model achieving a recall of 0.992.
  • The framework effectively handled imbalanced datasets and optimized classification performance.
  • The combination of EfficientNetV2S + ROS + MLP resulted in the lowest accuracy at 50.8%.

Conclusions:

  • The study validates the effectiveness of combining nature-inspired optimization, transfer learning, and ensemble machine learning for oral cancer detection.
  • The TR-ROS-BAT-ML framework presents a scalable, accurate, and efficient AI diagnostic tool.
  • Future work will explore multi-modal data integration for improved clinical applicability.