Oral squamous cell carcinoma detection using EfficientNet on histopathological images.

Journal: Frontiers in medicine
Published Date: Jan 29, 2024

Abstract

INTRODUCTION: Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model's objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization.

Authors

  • Eid Albalawi
    Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
  • Arastu Thakur
    Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India.
  • Mahesh Thyluru Ramakrishna
    Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India.
  • Surbhi Bhatia Khan
    Department of Data Science, School of Science, Engineering and Environment, University of Salford, Salford, United Kingdom.
  • Suresh SankaraNarayanan
    Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
  • Badar Almarri
    Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
  • Theyazn Hassn Hadi
    Applied College in Abqaiq, King Faisal University, Al-Ahsa, Saudi Arabia.

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