Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost

Abstract

The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide. An important step in combating COVID-19 is the screening of infected patients using chest X-ray (CXR) images. However, this task is extremely time-consuming and prone to variability among specialists owing to its heterogeneity. Therefore, the present study aims to assist specialists in identifying COVID-19 patients from their chest radiographs, using automated computational techniques. The proposed method has four main steps: (1) the acquisition of the dataset, from two public databases; (2) the standardization of images through preprocessing; (3) the extraction of features using a deep features-based approach implemented through the networks VGG19, Inception-v3, and ResNet50; (4) the classifying of images into COVID-19 groups, using eXtreme Gradient Boosting (XGBoost) optimized by particle swarm optimization (PSO). In the best-case scenario, the proposed method achieved an accuracy of 98.71%, a precision of 98.89%, a recall of 99.63%, and an F1-score of 99.25%. In our study, we demonstrated that the problem of classifying CXR images of patients under COVID-19 and non-COVID-19 conditions can be solved efficiently by combining a deep features-based approach with a robust classifier (XGBoost) optimized by an evolutionary algorithm (PSO). The proposed method offers considerable advantages for clinicians seeking to tackle the current COVID-19 pandemic.

Metaheuristics based COVID-19 detection using medical images: A review

“…This cost can be reduced by using metaheuristic optimization algorithms. Three studies by Hamdy et al [ 30 ], Júnior et al [ 32 ], Hanon et al [ 34 ] used PSO for parameter optimization.…”

“…Hamdy et al [ 30 ] and Júnior et al [ 32 ] have used particle swarm optimization (PSO) to solve image classification problem for COVID-19 diagnosis. Hamdy et al [ 30 ] worked with CT images, and solved a binary class problem by taking into account 1050 COVID-19 and 1500 normal images.…”

“…The CNN's hyper-parameters were tuned using PSO and obtained an accuracy of 98.04%. Júnior et al [ 32 ] also experimented with a binary classification problem. However, an X-ray dataset was used with only 206 COVID images, which were very few compared to Ref.…”

RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images

“…The danger of COVID-19 disease and its spread prompted researchers to develop many automatic diagnostic methods using X-ray images. Many traditional machine learning techniques have been presented in the literature for the early diagnosis of COVID-19 ( Heidari et al, 2020 , Li et al, 2021 , Sharifrazi et al, 2021 , Júnior et al, 2021 , Khan et al, 2021 , Fan et al, 2021 , Karthik et al, 2021 ). The convolutional neural network (CNN) ( Le Cun et al, 2015 ), support vector machine (SVM) ( Cortes and Vapnik, 1995 ), residual exemplar local binary pattern (ResExLBP), iterative ReliefF ( Li et al, 2021 ), and Sobel filter ( Sobel and Feldman, 1968 ) have been used.…”

COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images

“…Moreover, a computer-aided diagnostic combined approach based on graph CNN and pre-trained CNN model (Kumar et al [19]), a deep Covix-net model (Vinod et al [20]), new deep hybrid and deep boosted hybrid learning models (Khan et al [21]), and a gradient weighted class activation mapping technique (Panwar et al [22]) for coronavirus detection exhibited accuracies of 97.60%, 97%, 98.53%, and 95%, respectively. Likewise, a DenseNet-201 architecture reported by Alhudhaif et al [23], a PSO-based eXtreme Gradient Boosting model recommended by Dias Júnior et al [24], an automatic AI-based system using majority voting ensemble techniques suggested by Chandra et al [25], and a bi-level prediction model by Das et al [26] exhibited respective accuracies of 94.96%,98.71%, 91.329%, and96.74%to diagnose COVID-19. Other novel techniques to detect coronavirus include a deep LSTM model (Demir et al [27]), Inception-v3 model based on deep CNN associated with Multi-Layered Perceptron model called CovScanNet (Sait et al [28]), and a hybrid deep CNN technique with discrete wavelet transform features (Mostafiz et al [29].…”