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

Journal: Computers in biology and medicine
Published Date: May 5, 2025

Abstract

BACKGROUND: Oral cavity carcinoma remains a major public health concern, where early and accurate detection is vital for improving patient outcomes and survival rates. Current diagnostic systems often face challenges such as limited feature selection capabilities, imbalanced datasets, and computational inefficiencies.

Authors

  • Sakinat O Folorunso
    Artificial Intelligence Systems Research Group, Department of Computer Science, Olabisi Onabanjo University, Ago-Iwoye, Nigeria.
  • Akinshipo Abdulwarith
    Department of Oral and Maxillofacial Pathology and Biology, Faculty of Dentistry, University of Lagos, Lagos, Nigeria.
  • Abidemi Emmanuel Adeniyi
    Department of Computer Science, Bowen University, Iwo, Nigeria.
  • Halleluyah Oluwatobi Aworinde
    Department of Computer Science, College of Computing and Communication Studies, Bowen University, Iwo, Nigeria; Department of Information Technology, Durban University of Technology, Durban, KZ, South Africa.
  • Joseph Bamidele Awotunde
    Department of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin 240003, Nigeria.

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