Oral squamous cell carcinoma detection using EfficientNet on histopathological images

Eid Albalawi; Arastu Thakur; Mahesh Thyluru Ramakrishna; Surbhi Bhatia Khan; Suresh SankaraNarayanan; Badar Almarri; Theyazn Hassn Hadi

doi:10.3389/fmed.2023.1349336

Oral squamous cell carcinoma detection using EfficientNet on histopathological images

Front Med (Lausanne). 2024 Jan 29:10:1349336. doi: 10.3389/fmed.2023.1349336. eCollection 2023.

Authors

Eid Albalawi¹, Arastu Thakur², Mahesh Thyluru Ramakrishna², Surbhi Bhatia Khan^{3

4}, Suresh SankaraNarayanan¹, Badar Almarri¹, Theyazn Hassn Hadi⁵

Affiliations

¹ Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia.
² Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India.
³ Department of Data Science, School of Science, Engineering and Environment, University of Salford, Salford, United Kingdom.
⁴ Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.
⁵ Applied College in Abqaiq, King Faisal University, Al-Ahsa, Saudi Arabia.

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.

Methods: The research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies.

Results: The customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model's efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC.

Discussion: This research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model's ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC.

Keywords: EfficientNet; Oral Squamous Cell Carcinoma; cancer identification; diagnostic precision; histopathological images; microscopic imaging.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors extend their appreciation to the deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number INST 108.