Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm

Abstract

Osteosarcoma is one of the most common malignant bone tumors mostly found in children and teenagers. Manual detection of osteosarcoma requires expertise and it is a labour-intensive process. If detected on time, the mortality rate can be reduced. With the advent of new technologies, automatic detection systems are used to analyse and classify medical images, which reduces the dependency on experts and leads to faster processing. In this paper, an automatic detection system: Integrated Features-Feature Selection Model for Classification (IF-FSM-C) to detect osteosarcoma from the high-resolution whole slide images (WSIs) is proposed. The novelty of the proposed approach is the use of integrated features obtained by fusion of features extracted using traditional handcrafted (HC) feature extraction techniques and deep learning models (DLMs) namely EfficientNet-B0 and Xception. To further improve the performance of the proposed system, feature selection (FS) is performed. Here, two binary variants of recently proposed Arithmetic Optimization Algorithm (AOA) known as BAOA-S and BAOA-V are proposed to perform FS. The selected features are given to a classifier that classifies the WSIs into Viable tumor (VT), Non-viable tumor (NVT) and non-tumor (NT). Experiments are performed to compare the performance of proposed IF-FSM-C to the classifiers which use HC or deep learning features alone as well as state-of-the-art methods for osteosarcoma detection. The best overall accuracy of 96.08% is obtained when integrated features extracted using HC techniques and Xception are used. The overall accuracy is enhanced to 99.54% after applying BAOA-S for FS. Further, the application of BAOA-S for FS reduces the number of features with the best model having only 188 features compared to 2118 features if no FS is applied.

Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model

“…A detailed comparative analysis of the results offered by the WDODTL-ODC model with recent models is provided in Table 3 [ 24 , 25 ]. Figure 9 reports a brief examination of the WDODTL-ODC model with existing models.…”

A Comprehensive Survey on Arithmetic Optimization Algorithm

“…Dahou et al [117] and Bansal et al [118] developed binary version of AOA and employed for feature selection. The convolutional neural network (CNN) was utilized to learn and extract features from input data, and the Binary AOA was used to pick the best features for Human Activity Recognition [117].…”

Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

“…Radiomics feature is employed to automatically extract the segmented ROIs. In literature [12], the authors presented an automatic recognition model named Integrated Features-Feature Selection Model for Classification (IF-FSM-C) which diagnoses osteosarcoma in high-resolution Whole Slide Images (WSIs). In order to perform FS, two binary variations were used for the newly-presented Arithmetic Optimization Algorithm (AOA) such as BAOA-S and BAOA-V.…”