Vol.73, No.2, 2022, pp.3905-3920, doi:10.32604/cmc.2022.029326
OPEN ACCESS
ARTICLE
Deep Transfer Learning Driven Oral Cancer Detection and Classification Model
  • Radwa Marzouk1, Eatedal Alabdulkreem2, Sami Dhahbi3, Mohamed K. Nour4, Mesfer Al Duhayyim5, Mahmoud Othman6, Manar Ahmed Hamza7,*, Abdelwahed Motwakel7, Ishfaq Yaseen7, Mohammed Rizwanullah7
1 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University,Saudi Arabia
4 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Saudi  Arabia
5 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam Bin Abdulaziz University, Saudi Arabia
6 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New  Cairo, 11835, Egypt
7 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia
* Corresponding Author: Manar Ahmed Hamza. Email:
Received 01 March 2022; Accepted 26 April 2022; Issue published 16 June 2022
Abstract
Oral cancer is the most commonly occurring ‘head and neck cancers’ across the globe. Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public. Since earlier identification of disease is essential for improved outcomes, Artificial Intelligence (AI) and Machine Learning (ML) models are used in this regard. In this background, the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model (AIDTL-OCCM). The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques. The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing. Followed by, the densely-connected networks (DenseNet-169) model is employed to produce a useful set of deep features. Moreover, Chimp Optimization Algorithm (COA) with Autoencoder (AE) model is applied for oral cancer detection and classification. Furthermore, COA is employed to determine optimal parameters involved in AE model. A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects. The extensive experimental analysis outcomes established the enhanced performance of AIDTL-OCCM model compared to other approaches with a maximum accuracy of 90.08%.
Keywords
Oral cancer detection; image classification; artificial intelligence; machine learning; deep learning
Cite This Article
R. Marzouk, E. Alabdulkreem, S. Dhahbi, M. K. Nour, M. Al Duhayyim et al., "Deep transfer learning driven oral cancer detection and classification model," Computers, Materials & Continua, vol. 73, no.2, pp. 3905–3920, 2022.
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