Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

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.