Open Access

ARTICLE

Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

by Mesfer Al Duhayyim^1,*, Areej A. Malibari², Sami Dhahbi³, Mohamed K. Nour⁴, Isra Al-Turaiki⁵, Marwa Obayya⁶, Abdullah Mohamed⁷

1 Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia
2 Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha, 62529, Saudi Arabia
4 Department of Computer Science, College of Computing and Information System, Umm Al-Qura University, Mecca, 24382, Saudi Arabia
5 Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh, 4545, Saudi Arabia
6 Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
7 Research Centre, Future University in Egypt, New Cairo, 11845, Egypt

* Corresponding Author: Mesfer Al Duhayyim. Email:

Computer Systems Science and Engineering 2023, 45(1), 753-767. https://doi.org/10.32604/csse.2023.030556

Received 29 March 2022; Accepted 30 April 2022; Issue published 16 August 2022

Abstract

Recently, computer aided diagnosis (CAD) model becomes an effective tool for decision making in healthcare sector. The advances in computer vision and artificial intelligence (AI) techniques have resulted in the effective design of CAD models, which enables to detection of the existence of diseases using various imaging modalities. Oral cancer (OC) has commonly occurred in head and neck globally. Earlier identification of OC enables to improve survival rate and reduce mortality rate. Therefore, the design of CAD model for OC detection and classification becomes essential. Therefore, this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification (CADOC-SFOFC) model. The proposed CADOC-SFOFC model determines the existence of OC on the medical images. To accomplish this, a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network (ResNet) model. Besides, feature vectors are fused and passed into the extreme learning machine (ELM) model for classification process. Moreover, SFO algorithm is utilized for effective parameter selection of the ELM model, consequently resulting in enhanced performance. The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%. Therefore, the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.

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