COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

Abstract

A novel strain of Coronavirus, identified as the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), outbroke in December 2019 causing the novel Corona Virus Disease (COVID-19). Since its emergence, the virus has spread rapidly and has been declared a global pandemic. As of the end of January 2021, there are almost 100 million cases worldwide with over 2 million confirmed deaths. Widespread testing is essential to reduce further spread of the disease, but due to a shortage of testing kits and limited supply, alternative testing methods are being evaluated. Recently researchers have found that chest X-Ray (CXR) images provide salient information about COVID-19. An intelligent system can help the radiologists to detect COVID-19 from these CXR images which can come in handy at remote locations in many developing nations. In this work, we propose a pipeline that uses CXR images to detect COVID-19 infection. The features from the CXR images were extracted and the relevant features were then selected using Hybrid Social Group Optimization algorithm. The selected features were then used to classify the CXR images using a number of classifiers. The proposed pipeline achieves a classification accuracy of 99.65% using support vector classifier, which outperforms other state-of-the-art deep learning algorithms for binary and multi-class classification.

Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation

“…In this paper, we follow deep transfer learning employing the pre-trained SqueezeNet model which is trained on the ImageNet dataset. We further fine-tune this network training it with the training dataset constructed from Singh et al (2021) . The SqueezeNet model is initially trained on the training dataset for binary and three class classifications and tested with the respective test datasets.…”

“…Along with examinations of symptoms and pathogenic testing, imaging examinations are found indispensable in the diagnosis in the screening, detection, diagnosis and prognosis of COVID-19 ( Aradhya et al, 2021 , Bhapkar et al, 2021 , Dey et al, 2020 , Kaiser et al, 2021 , Murugappan et al, 2021 , Singh et al, 2021 ). In a study on imaging modalities in the diagnosis of COVID-19, Yang et al (2020) have shown that Computed Tomography (CT) images are very effective in capturing Ground Glass Opacity (GGO), consolidations and patchy areas in the peripherals of the lungs, in the early and advanced stages of infections.…”

Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models

“…It was found that the SVR and SEL were the best in accuracy terms [ 28 ] 2020 Supervised learning Classification Logistic regression, decision tree, support vector machine naive Bayes, and artificial neutral network The findings were stated that, the correlation coefficient analysis between various dependent and independent features was carried out. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% [ 29 ] 2021 Supervised learning Classification Hybrid social group optimization and support vector classifier In this work, they propose a pipeline that uses CXR images to detect COVID-19 infection. The features from the CXR images were extracted and the relevant features were then selected using Hybrid Social Group Optimization algorithm.…”

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

“…The accuracy is about 0.98 using KNN as a classifier. In Singh et al [ 128 ], the hybrid social group optimization (HSGO) method was used to select the features, and several different classifiers were used for classification. SVM with 99.65% accuracy has been named as the best classifier.…”