Open Access

ARTICLE

Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

Areej A. Malibari¹, Jaber S. Alzahrani², Marwa Obayya³, Noha Negm^4,5, Mohammed Abdullah Al-Hagery⁶, Ahmed S. Salama⁷, Anwer Mustafa Hilal^8,*

1 Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
3 Department of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
4 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5 Faculty of Science, Mathematics and Computer Science Department, Menoufia University, Egypt
6 Department of Computer Science, College of Computer, Qassim University, Saudi Arabia
7 Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, 11845, Egypt
8 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computers, Materials & Continua 2022, 73(3), 6443-6459. https://doi.org/10.32604/cmc.2022.031324

Received 14 April 2022; Accepted 07 June 2022; Issue published 28 July 2022

Abstract

Osteosarcoma is a type of malignant bone tumor that is reported across the globe. Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the detection and classification of malignancies in biomedical images. In this regard, the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning (BOIC-EHODTL) model. The presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of osteosarcoma. At the initial stage, Gabor Filter (GF) is applied as a pre-processing technique to get rid of the noise from images. In addition, Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature vectors. Then, EHO algorithm is utilized along with Adaptive Neuro-Fuzzy Classifier (ANFC) model for recognition and categorization of osteosarcoma. EHO algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification results. The design of EHO with ANFC model for classification of osteosarcoma is the novelty of current study. In order to demonstrate the improved performance of BOIC-EHODTL model, a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.

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Keywords

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