key: cord-0723603-fvergmd0
authors: Bhardwaj, Prashant; Kaur, Amanpreet
title: A novel and efficient deep learning approach for COVID‐19 detection using X‐ray imaging modality
date: 2021-07-21
journal: Int J Imaging Syst Technol
DOI: 10.1002/ima.22627
sha: 9ee43d8163bb1f79b81e7a2266141b9590229567
doc_id: 723603
cord_uid: fvergmd0

With the exponential growth of COVID‐19 cases, medical practitioners are searching for accurate and quick automated detection methods to prevent Covid from spreading while trying to reduce the computational requirement of devices. In this research article, a deep learning Convolutional Neural Network (CNN) based accurate and efficient ensemble model using deep learning is being proposed with 2161 COVID‐19, 2022 pneumonia, and 5863 normal chest X‐ray images that has been collected from previous publications and other online resources. To improve the detection accuracy contrast enhancement and image normalization have been done to produce better quality images at the pre‐processing level. Further data augmentation methods are used by creating modified versions of images in the dataset to train the four efficient CNN models (Inceptionv3, DenseNet121, Xception, InceptionResNetv2) Experimental results provide 98.33% accuracy for binary class and 92.36% for multiclass. The performance evaluation metrics reveal that this tool can be very helpful for early disease diagnosis.

Severe Acute Respiratory Syndrome 2 (SARS-CoV-2), the name given by the International Committee on Taxonomy of Viruses (ICTV), instigates the novel coronavirus or COVID-19. Recently COVID-19 has become a global health emergency that has appeared in Wuhan, China in late December 2019. Coronavirus is one of the significant pathogens that fundamentally focus on the human respiratory framework and is highly infectious to the health of individuals. The virus later transformed into a worldwide pandemic, as declared by World Health Organization (WHO). 1 In the primary stage of disease transmission, the number of people affected by the disease was minimum; it did not imitate threats of such huge capacity. 2 With the slow progression of time, the virus spread with an extremely high-risk potential of affecting millions of lives in all countries, and become the principal factor of many people's death globally. 3 Due to high mortality rate by COVID-19 cases worldwide, countries with more number of active cases suggest people to stay indoors and announced a complete lockdown to stop disease transmission. 4 COVID-19 infection is fatal because it is easily transmitted through direct or indirect contact with the affected individual and its symptoms include fever, dry cough, vomiting, diarrhea, and myalgia as indicated by WHO and Centre for Disease and Control (CDC). Till date, May 2021, the COVID-19 pandemic already contributed to over 3 170 882 deaths and more than 150 779 711 confirmed cases of COVID-19 infection. 5 Researchers are actively participating to detect the COVID-19 positive cases and also finding the diagnosis procedure and medical treatment of affected patients rapidly. Daily increment of positive COVID-19 cases and wrong diagnosis is challenging in managing the pandemic. With large number of infected individuals and less number of test kits, practitioners totally rely upon automated detection system to combat the pandemic proficiently at early stage. These detection systems could also be important in identifying patients who need isolation to prevent the disease from spreading in the community. The laboratory tests used for COVID-19 detection including nucleic acid reagent detection, viral antigen detection were timeconsuming and produce a higher false-negative detection rate.

Reverse transcription-polymerase chain reaction (RT-PCR) test-kits have emerged as the main technique for diagnosing COVID-19. The current RT-PCR system is time-consuming and also it requires additional resources and approval to detect infected patients, which can be more costly in many developing societies. Due to the inaccessibility of test kits and the false-negative rate of virus and antibody tests, the medical authorities have temporarily used radiological examinations as a clinical investigation for COVID-19 detection but there are limited kits for detecting the virus efficiently. 6 These issues have posed a great life threat, especially in societies with limited medical assets. However, noting the recent spread of COVID-19, the researchers need to look for other better options like X-ray and computed tomography (CT) scans. The information obtained from radiological images, that is, X-ray and CT scans is important for clinical diagnosis. Radiological images have high sensitivity in disease diagnosis and more accurate than RT-PCR. Thus, radiologists can notice the characteristics of a lung infected with COVID-19 by using chest X-ray and CT scan. 7 CT scan causes multiple problems of healthcare when requiring multiple scans during the course of disease. The American College of radiology disapproves the use of CT scan as a first line of diagnosis. So the medical practitioners recommended chest X-ray (CXR) than CT scan radiography. 8 CXR requires less expensive equipment's and also can be used in an isolated room and prevent the risk of infection to other persons. 9,10 Therefore, it is worth combining these radiographic images with artificial intelligent (AI) system for better and accurate predictions of COVID-19. 11 Machine learning (ML) and deep learning (DL) were the two sub-categories of AI that have been used for detection of various diseases. 12 The image processing technology has gained immense momentum in all sectors of healthcare, especially in the field of lung disease detection. 13, 14 Hence these methods have been a natural choice for COVID-19 research as well. Other testing methods namely chest X-ray, CT scans, are also being considered by many nations to aid diagnosis and provide evidence of more serious disease progression. 6, 15, 16 Inspired by this, several investigators and sources recommended the use of chest radiographs for detection of COVID-19. 17, 18 Thus, radiologists can notice the characteristics of a lung infected with COVID-19 by using radiographic data. 19 Several applications using DL approaches have already been proposed as an attempt to address COVID-19 detection from chest X-ray. 20 As various studies cited in the related work section revealed that chest X-ray images have the potential to monitor and examine various lung diseases such as tuberculosis, infiltration, atelectasis, pneumonia, and hernia. Also chest X-ray diagnosis systems are cheap and widely available. COVID-19, which shows as an upper respiratory tract and lung infection can also be detected by using chest X-ray imaging modality. The present study focuses on using different DL methods to combat the COVID-19 pandemic using an automated detection system for accurate and fast decision making, as self or manual reading of chest radiological (X-ray) data of infected patients take a significant time. Our research coined with DL ensemble model to solve binary class (COVID-19 vs. non-COVID) and multiclass (COVID-19 vs. pneumonia vs. non-COVID) problems. The proposed ensemble model uses various pre-trained deep neural networks for deep feature extraction. These DL algorithms previously employed in many image classification and computer vision problems. After feature extraction, different ensemble models were used to produce the final prediction.

The key contributions of this paper are as follows:

• We develop an ensemble learning based system using deep convolutional neural network which is trained and evaluated on publically available chest X-ray image dataset having the knowledge to classify between Covid vs. normal vs. pneumonia subjects. The limited number of COVID-19 subjects used by many researchers in their work [21] [22] [23] leads to degrading the model efficiency as with lower COVID-19 subjects, the severity of the disease is not properly diagnosed. A large medical image dataset is used in our work and the proposed model shows promising results in terms of accuracy and other evaluation metrics (precision, recall, and F-1 score). • We have used four (Inceptionv3, DenseNet121, Xception, and InceptionResNetv2) powerful and efficient DL models with more number of training parameters that are coupled with global based features for classifying COVID-19 subjects from other classes and reduce the misdiagnosis rate for COVID-19 and help the doctors, field specialists, and physician to know the severity of disease at early stage. • Data augmentation methods are employed for COVID-19 detection to avoid overfitting problems. We have finetuned our model by using four pre-trained models in depth to compensate for the loss of valuable information.

Recently, there have been numerous AI learning-based methods that were used for COVID-19 detection using radiographic data. In the medical care field, DL which is a sub-branch of AI is in effect and gradually developed as a Computer-Aided Design (CAD) tool to help doctors/ radiological experts for better disease prediction. 24 DL methods can be a guide for professionals to improve the quality of COVID-19 detection. 21, 23, 25, 26 Already many DL methods have been applied in the healthcare field 8, 27 to address a variety of issues such as COVID-19 detection using X-ray images and CT scans. 9,10,23,28-31 Also newly modified CNN methods have been proposed for COVID-19 detection such as COVID-Net, 21 CoroNet, 26 CovidxNet, 29 COVIDiagnosis-Net, 32 DarkCovidNet, 23 and nCOVNet. 33 44 have proposed a modified covidnet named EDL-net model. The three-class dataset contains; pneumonia (6053), COVID-19 (573), and normal (8851) images. The detection accuracy of 95% has been obtained for the proposed model. DarkCovidNet has been proposed in Ozturk et al. 23 to detect COVID-19 for three-class datasets comprising 125 COVID-19, 500 pneumonia, and 500 normal images with a detection accuracy of 87.2%, using the 5-fold cross-validation to avoid over-fitting. Khan et al. 26 proposed CoroNet CNN to detect COVID-19 from X-ray images having 4-class image dataset, that is, 284 COVID-19, 310 normal, 330 bacterial pneumonia, and 327 viral pneumonia. For this dataset, detection accuracy of 89.6% was obtained with a 4-fold cross-validation technique performed on Google Collaboratory with Tesla K80 graphics card. Gunraj et al. 21 developed COVID-Net and validated it on a dataset of 358 COVID-19, 5538 normal, and 8066 pneumonia images with a sensitivity rate of 91% for COVID-19 detection with 70% for training and 30% data for testing respectively. It should be noticed that the three-class images were not balanced. Panwar et al. 33 proposed a model named nCOVNet for COVID-19 detection with binary classes having a dataset of 142 normal and 142 COVID-19 images. The dataset has been divided into two parts, that is, 70% for training and 30% for testing and detection accuracy of 88% was obtained. An initial attempt by Sethy and Behera 39 was utilized on 3-class with 127 (COVID-19, pneumonia, and normal) images. The dataset has been divided into 80% for training and 20% for testing. ResNet-50 model was used with support vector machines (SVM) classifier and an accuracy of 95.33% has been obtained. Afifi et al. 45 in their study utilize three pre-trained network (Resnet18, densenet161, and inceptionv4) for COVID-19 detection. The experimental results revealed that their model achieved an accuracy of 91.2% for three-class problem. Rafi 46 used an ensemble DL model with a chest X-ray image dataset of 5907 images. Approximately 500 Covid images were used in their work. Their model achieved an accuracy of 98% for multiclass problem. Kesim et al. 47 proposed a CNN-based model for the classification using chest X-ray image. A chest X-ray dataset from 12 categories has used, with an accuracy rate of 86% reported in experimental results. Sedik et al. 48 proposed a CNN and long short term memory (LSTM) based model named as ConvLSTM. Two different datasets were used in their study. The proposed model was tested on two different imaging modalities X-ray and CT scans. The model has been evaluated on two different scenarios (Covid vs. normal and Covid vs. pneumonia) and achieved accuracy of 100% obtained.

Bhandary et al. 49 modified the AlexNet model to detect lung abnormalities based on chest X-ray images. More specifically, the authors used a DL approach to screen for pneumonia. A new "threshold filter" has been presented and a feature ensemble strategy has also been defined that produced a 96% classification accuracy rate. Chouhan et al. 7 presented five new deep-transfer-learning-based models applied as an ensemble to detect pneumonia in chest X-ray images. The authors reported an accuracy score of 96.4% using their developed ensemble deep model. Kumar et al. 50 utilizes ResNet152 pre-trained model with XGBoost classifiers and evaluated it on a 3-class problem containing 1341 normal images, 1345 pneumonia images, and only 62 COVID-19 images with 30% holdout. Their model achieved an accuracy of 97.3%. Masud et al. 51 proposed an Internet of Medical Things (IoMT) based lightweight security model to help the medical practitioners for COVID-19 detection in a more efficient way. In their work strength of Mutual Authentication and Secret Key (MASK) have been evaluated to prevent the physical attacks and increase the computational efficiency. Öksüz et al. 52 proposed a DL model using three pre-trained CNNs (SqueezeNet, ShuffleNet, and EfficientNet-B0). The total of 2905 CXR samples, including 1345 samples for pneumonia, 219 for COVID-19, and 1341 for normal samples were used in the study. The classification accuracy of 98.30 has been achieved for multiclass problem. Li et al. 22 proposed an automatic system named as COVNet using DL methods for COVID-19 detection. The pre-trained CNN architecture named ResNet50 was used in the study. A total of 4536 chest CT samples, including 1296 samples for COVID-19, 1735 for pneumonia, and 1325 for normal samples were used in the study. The dataset has been divided into two parts, that is, 90% for training and 10% for testing respectively. The experimental result revealed that the system obtained a sensitivity of 90%, AUC of 96%, and specificity of 96% for COVID-19 cases.

Following are the limitations that we have addressed while going through the previously explored literature for COVID-19 dtection:

1. COVID-19 cases should need to be combined with other lung diseases dataset like TB, lung cancer, etc. so that the severity of disease can be identified in a better manner. 2. The small COVID-19 image dataset leads to unbalanced class problem in comparison to other diseases considered in the obtained dataset.

3. The need for GPU resources to train newly designed CNNs or pre-trained CNNs. Also, the deeper features of COVID-19 are not known to be separated, when a greater number of diseases are being considered.

Considering all those factors, we can say that there is a need for effective detection system for COVID-19 detection. As the spread of this deadly virus has increased demonically and taken the lives of many people residing in different countries. The current COVID-19 screening method is RT-PCR. This is the first method that is used by many practitioners/doctors to diagnose COVID-19. But the problem with this method is that it is very timeconsuming and the results obtained from this method take few days to weeks. This creates a major challenge with less equipped clinics or hospitals. When compared to PCR techniques, chest radiographic imaging (CRI) methods have many advantages, that is, they are easily available and cheap. The use of radiographic imaging systems plays an important role in those areas where appropriate test kits are not available. Quality of radiographic images depends on the digital devices. 53 Also chest radiograph technique plays a great role as an image retrieval model based on deep metric learning, where images of the same contents are pulled together. 54 This technique has incredible clinical value for the treatment and management of COVID-19 patients. With the improvement of this model visual saliency-guided complex image retrieval model can be utilized to get the image patterns more clearly. 55 CRI methods include X-ray and CT scans. In a small number of tests, CT has been used to analyze and detect features of COVID-19 with more clarity, that is, due to its high-resolution value for lung uniformity and ground-glass opacity. However, due to more cost and less availability of CT machines in rural areas, it may not be a good choice. 19 Whereas, an X-ray test can be considered an ideal solution to detect COVID-19, as it is more available at a lower cost. But, it can be a challenging task for a radiologist for discovering X-ray images to differentiate between community-acquired pneumonia (CAP), COVID-19, and other lung-related diseases. Due to the increased rush of patients in hospital emergency rooms (ERs), accurate disclosure of radiographic data is mandatory, as it can save a lot of time. In the current section, we address the above-mentioned problem and present an approach to handle this problem more effectively.

Ensemble-based DL model is proposed for COVID-19 detection using four pre-trained architecture as mentioned above. Ensemble learning 56 is a newly emerging field of AI including ML and DL. 57, 58 From the last few decades, ensemble system also known as multiple classifier system has gained everyone's attention in the field of AI. As these systems have proven to be very effective to solve many real-world problems like in healthcare field and many computer vision problems. These systems combine the features of multiple models to boost the overall efficiency of the system by reducing system error. The different models adapt diverse features of each other and are grouped to make more accurate predictions using classifiers. Ensembling is a two-way process. In the first step, deep features of a network are extracted using pre-trained architectures. In the next step, classifiers are used to make accurate predictions. The ensemble network always gives more accurate results as compared to a single model. 56 Several classifiers like decision tree, K-nearest neighbor, SVM, auto encoders, Boltzmann Machine (BM), etc. used in many ML and DL models to improve model efficiency. The main motivation for using the classifier is to extract deep features from the image that may lead to improve the model performance. It uses various algorithms to train the data set and make the final prediction based on the clustering. The concept of voting technique was used in this research work. The voting group collects the decisions of many classifiers and performs a particular classification task; provides flexibility in clustering strategies so that maximum possible classification accuracy is obtained. 59 Voting techniques can be divided into two categories: hard and soft voting. 60 In hard voting method the class labels of the test samples are absolute by the majority voting method. Each base classifier independently gives a class label to a given test sample during the testing phase. The final grouping of the test sample is determined by the maximum number of times a particular class label is assigned to that test sample. 61 On the other hand, soft voting methods calculate the average probability of all classes, and the final prediction is made on the basis that which class is having the highest probability. 60 As these techniques do not use any algorithm for combining predictions from base classifiers as required in the stacking set, 61 this makes them a good choice to be used in ensemble models.

The layout of the proposed methodology is shown in Figure 1 . As seen from Figure 1 powerful CNN model (inceptionv3, inceptionresnetv2, densenet121, and xception) with more number of trainable parameters is used to fine-tune the proposed model and simply extracts F I G U R E 1 Proposed classification framework more features in depth. The detailed summary of the proposed method is given in the following section.

In this study, CRI based dataset is used. The dataset consists of 10 000 X-ray images in Portable Network graphics (PNG) format. The resolution of each image is set to 224 Â 224 Â 3. The different chest radiographs are combined into one dataset which can be used for classification purposes. Pneumonia dataset was taken from kaggle repository, whereas COVID-19 and non-COVID dataset was taken from previous publications 23 and online available resources. The final dataset contains overall of 10 000 CXR images in which 2022 sample belongs to pneumonia class, 2161 to COVID-19 and 5863 to non-COVID class. In this study, to develop a robust and deep efficient model to perform classification task, we employed the use of 5-cross validation. To perform this cross-validation the overall samples are distributed into three sets; training (80%), testing (10%), and validation (10%). The dataset distribution for different classes is shown in Table 1 .

Pre-processing methods can be valuable for eliminating undesirable noise present in the given image. In the present work, contrast enhancement and image normalization method is applied as shown in Figure 2A used to change pixel intensity value for acquiring a better-enhanced image. By changing the pixel intensity, hidden information that exists within the low range of gray level image is revealed. On the other hand, data augmentation methods were also employed to augment training samples by creating diverse data without losing useful information. The main reason to use different image augmentation methods is that it increase the overall performance of the system by adding more diverse data to the limited dataset. Data augmentation methods including Image rotation, image scaling, flipping, and translation were applied to the original dataset as shown in Figure 2B . These acts as a stabilizer and reduce over-fitting problems while training our DL model.

In the previous many years, rise of deep convolutional networks has brought a big breakthrough in many filed of image processing including computer vision and machine vision tasks. Deep neural networks are used to extract deep features or the most valuable information from an input data. These types of networks are used to solve big data problem and usually trained on a higher dataset. Recently, these networks made a great impact in many fields including industries and healthcare. 62 Most preferred and usable CNN are DenseNet, 63 Xception, 26 Inception, 64 and Resnet. 43 These are the pre-trained neural networks and already trained in ImageNet dataset. The features extracted from them are transferrable to a newly designed model. These networks are very useful while training a new architecture from scratch, as the weights used in these pre-trained architectures can further be used in a newly designed architecture. There are many inputs, hidden and output layers present in a network. Input layers are used to give input data to any model and hidden layers are used to extract the important features, whereas output layers are used to make the final classification. In our work, we employed four different pre-trained models, InceptionV3, DenseNet121, InceptionResNetV2, and Xception for COVID-19 detection.

A CNN architecture that is widely used for image recognition problems. Inception v3 has 24 million parameters and achieved a good accuracy on ImageNet dataset. This network used a factorization method for computation and provides more accurate results. Keras is the prime host of Inceptionv3 network and plays an important role in its structure development. The main motive behind the use of this network is to avoid representational bottlenecks, that is, reducing the input dimensions of the next layer. Different convolution and pooling layer used in the inceptionv3 architecture is shown in Figure 3 .

DenseNets are called the densely connected CNN's. These networks generally alleviate the vanishing gradient problem and decrease the parameter usage to extract more features. In our study we use DenseNet121 architecture. It is a CNN architecture with 121 deep layers. More number of layers used in the network makes the network very deep but at the same time makes it more effective by using shorter connections between layers. Maximum information can be shared through these connections. It works in a hierarchical manner as each layer of this network is connected to its alternate layers, that is, the first layer j1 is connected to j2, j3, j4, and the second layer j2 to j3, j4, j5, and so on. ( j À 1)th layer of the activation map is considered as the input of jth layer. But one thing needs to be taken out the input convolutional layer must be equal to output convolutional layer as shown in Figure 4 . Mathematically, it can be given as:

where Wj represents the input jth layer and Yj represents the output of jth layer. These networks use a concatenation process for combining the features of a network. Transition layer and batch normalization functions are used to stabilize the learning process.

Inceptionresnet v2 is a pre-trained CNN architecture that builds on the inception family but includes residual connections. This network consists of 56 million parameters with 164 deep layers and obtained better results for ImageNet dataset. They are generally used for image classification, image segmentation, and object detection. The layout of the inception resnetv2 architecture used in this study is shown in Figure 5 .

Xception is a CNN architecture that was firstly used in 2017. Xception network is the modified version of inception and ranked third on ImageNet dataset challenge. 65 Instead of conventional convolution, this network uses depth wise convolution layers which involves mapping of crosschannel and spatial correlations. This network consists of 22.9 million parameters and used for many image classification and object detection problems. The main motive to design this architecture is to create a network with more parameters that can be used to solve any computer network (CN) problem. The layout of the xception architecture used in this study is shown in Figure 6 . The value for test dataset was kept changing with new input images while the training and validation dataset was kept constant. As mentioned in Table 2 , 5-cross validation is performed on training and testing dataset using stochastic gradient descent (SGD) as an optimizer function with a learning rate of 0.001. All the pre-trained CNNs are trained with input (CXR) images for many epochs to stabilize the loss function. The test dataset was used to get the final classification accuracy of the proposed model.

The proposed model performance is illustrated in the form of confusion metrics (CM) and receiver operating characteristics (ROC). Accuracy, precision, recall, MCC, F-score are also used to evaluate the performance of network and are given as:

Accuracy: It is the most important performance measure. It can be calculated as a ratio of true predicted observation with respect to the total observations. Accuracy of the system can be shown mathematically by using the formula:

where TP denotes the true positive labels, TN denotes the true negative labels, FN denotes the false negative labels, and FP denotes the false positive labels. Precision: It is the ratio of true predicted positive observations with respect to the total predicted positive observations

Recall: It is the ratio of true predicted positive observations with respect to the total observations in both positive and negative class. Mathematically, it can be given as:

F-1 score: It can take weighted average value of precision-recall. Mathematically it can be calculated using the formula:

MCC: It stands for Matthews's correlation coefficient. It is a single value metric that reviews the confusion matrix. It consists of four different entries: true negatives (TN), true positive (TP), false negatives (FN), and false positives (FP). It defines a relationship between actual and predicted classes. Mathematically, MCC can be given as: 

This section provides testing and performance analysis to verify the efficiency suggested technologies to detect COVID-19. The calculation of different evaluation parameters for three-class scenarios is presented in Table 3 that for binary and multiclass problem inceptionresnetv2 model shows a promising accuracy of 96.28% and 88.19% respectively using ensembling methods. Five-fold cross-validation method is employed to examine the deep CNN architectures to boost the overall performance of the proposed model. The complete CXR image dataset is split into three sets as mentioned above to avoid the problem of overfitting. The value of each individual model is calculated for 5-folds is averaged and the mean value is used calculate other evaluation metrics. After performing 5-fold cross-validation, the performance evaluation metrics for binary and multiclass is achieved as in Table 4 . The proposed ensemble system for COVID-19 detection achieved highest overall accuracy of 98.45% and 92.36 for binary and multiclass respectively using 5-fold crossvalidation.

The trials to recognize and classify COVID-19 cases using X-ray imaging modality are divided into two different setups. At first, the ensemble model is trained to identify binary class problems including COVID-19 and non-COVID sets. Secondly, the ensemble DL model is trained to classify multiclass problems including COVID-19, non-COVID, and pneumonia. The effectiveness of the offered model is calculated using different ensembling techniques for both binary and multiclass classification problems. The dataset has been divided into three categories: 80% of X-ray image data utilized for training, 10% for testing, and 10% data for validation. Figure 7A ,C represents the confusion matrix for binary and multiclass respectively using simple averaging approach. Whereas Figure 7B ,D represents the confusion matrix for multiclass problem using weighted ensembling technique. The confusion matrix is an N Â N matrix that is used to calculate the overall proficiency of the suggested model, where N represents the number of target groups. The matrix compares the actual target values (True label) with the predicted values. Horizontal axis denotes the value of true label and vertical axis denotes the value of predicted level for different classes: 0 (COVID), 1 (normal), and 2 (pneumonia). True label are the values which already sets to true, that is, there will no change in these during the classification task, but predicated label are used to make predictions. The numerical values in the confusion matrix indicate the value for TP, FP, FN, and FP. These labels are used to calculate different evaluation metrics of the proposed model. On the other hand, Figure 7E ,G shows the precision-recall curve for binary class problem and also the relationship between different threshold values of precision and recall. Similarly Figure 7F ,H represents the precision-recall curve for multi class problem. Different colors were used to represent the area under the curve for different classes. Maroon color denotes the area under the curve for class 0 (COVID), blue color denotes class 1 (normal), and green color denotes class 2 (pneumonia). The maximum area under the curve represents higher precision and recall value. The maximum precision value shows low false positives, whereas the maximum recall value specifies low false negatives. It can be seen from the graphs that there is a substantial growth in loss values at the initial stage of training. In the advanced stage of training, these loss values decrease substantially. However, the deep model inspects all the X-ray images Therefore the execution is carried out by different ensembling techniques, that is, simple averaging and weighted averaging detection.

The ROC curve is used as another rating scale to provide an accurate visualization of simulation results. Within the ROC curve, the TP labels are symbolized as a utility of the FP labels at distinct cut-off points. No intersection in the given two distributions indicates that the ROC curve crosses through the upper left corner. Hence, from Figure 8 it can be the closest the ROC curve is to the upper left corner, the greater the efficiency of the given system. Figure 8A ,C represents the value of ROC curve using simple averaging technique and Figure 8B ,D represents the value of ROC curve using weighted averaging approach. Different color notations: purple for class 0 (COVID-19), sky blue for class 1 (normal), and yellow for class 2 (pneumonia) were used to mark the curve lines. And it is clear from Figure 8A ,B that the area value for binary classes is more as compared to multiple classes. The more the area of the ROC curve, the better the model performance.

In this section, we make assessments of the pre-trained DL architectures that have been proposed to detect COVID-19 to date against our proposed ensemble model. In Table 5 some of the latest approaches used for COVID-19 detection are considered. Ozturk et al. 23 presented a DL model for COVID-19 detection using X-ray imaging modality. From the experimental results, it was seen that accuracies of 98.08% and 87.02% were achieved respectively for binary and multiclass problems. Bhandary et al. 49 improved the AlexNet model for the detection of lung anomalies using chest X-ray images. The authors used a DL approach for the detection of pneumonia. A new "threshold filter" has F I G U R E 8 (A, C) Receiver operating characteristics (ROC) curve using simple averaging (B, D); ROC curve using weighted averaging [Color figure can be viewed at wileyonlinelibrary.com] also been introduced with ensemble learning which produced a 96% classification accuracy rate. Sethy and Behera 39 proposed a model for multiclass problem including 127 (COVID-19, pneumonia [viral and bacterial], and normal) images. The dataset has been divided into two parts, that is, 80% for training and 20% for testing. ResNet-50 model was used in their work with SVM classifier and an accuracy of 95.33% was obtained. Gianchandani et al. 66 proposed a modified DL model using four pre-trained CNNs (DenseNet201, ResNet152V2, InceptionResNetV2, and VGG16). Two different kinds of image datasets D1 and D2 have been employed for multiclass problem. For binary and multiclass, accuracies of 96% and 99% have been obtained respectively. Rahimzadeh and Attar 43 proposed a DL model based on two CNN pre-trained architectures Xception and resnet50v2. The dataset used in the study includes 15 085 (COVID-19 = 180, normal = 8851, pneumonia = 6054) images. Multiple features were extracted to boost the overall performance of the proposed model. The classification accuracy of 91% has been obtained for multiclass problem. From Table 5 , it is clear that the projected approach has worked well in terms of accuracy and other evaluation metrics on a higher chest X-ray image dataset when compared with other DL approaches.

The recent epidemic has changed human lives to an extraordinary extent and has become a global health problem. Though the attempt of the academic community has been tremendous through different fronts, the virus progression occurs at a rapid rate. Different DL and ML algorithm has been developed to diagnose the virus at an early stage. Because COVID-19 is highly infectious, it is important to control its transmission path effectively to prevent the spread of the disease. In the proposed work, we present a deep ensemble learning architecture for COVID-19 detection using four different pre-trained deep neural network architectures (inceptionv3, den-senet121, inceptionresnetv2, and xception). The model has been trained on CXR images to check the robustness of the proposed model. Data augmentation methods have been employed, in order to avoid the problem of limited dataset. We also validate our model using 5-fold crossvalidation for binary and multiclass problems including three scenarios: COVID-19, pneumonia, and normal and obtained the best results. At last, we can conclude that this technique can be a valid tool that can be helpful for doctors and researchers to detect COVID-19 efficiently. 

The data that support the findings of this study are available from the corresponding author upon reasonable request.

ORCID Prashant Bhardwaj https://orcid.org/0000-0002-9496-5326

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Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images

Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning

Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information

Application of deep learning for fast detection of COVID-19 in X-rays using nCOVNet

Densely connected convolutional networks

MobileNetV2: inverted residuals and linear bottlenecks

ImageNet large scale visual recognition challenge

Automatic tuberculosis screening using chest radiographs

Identifying medical diagnoses and treatable diseases by image-based deep learning

Detection of coronavirus disease (covid-19) based on deep features

Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays

Sensor applications and physiological features in drivers' drowsiness detection: a review

A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and Res-Net50V2

EDL-COVID: ensemble deep learning for COVID-19 cases detection from chest X-ray images

An ensemble of global and local-attention based convolutional neural networks for COVID-19 diagnosis on chest X-ray images

An ensemble deep transfer-learning approach to identify COVID-19 cases from chest X-ray images

X-ray chest image classification by a small-sized convolutional neural network

Efficient deep learning approach for augmented detection of Coronavirus disease

Deep-learning framework to detect lung abnormality -a study with chest Xray and lung CT scan images

Accurate prediction of COVID-19 using chest X-ray images through deep feature learning model with SMOTE and machine learning classifiers

A lightweight and robust secure key establishment protocol for internet of medical things in COVID-19 patients care

Ensemble-CVDNet: a deep learning based end-to-end classification framework for COVID-19 detection using ensembles of networks. ArXiv

Impact of digital fingerprint image qualityon the fingerprint recognition accuracy

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19

Visual saliency guided complex image retrieval

A survey of the recent architectures of deep convolutional neural networks

ImageNet classification with deep convolutional neural networks

Combining classifiers

Ensemble learning technique for cloud classification

A comparative study of heterogeneous ensemble methods for the identification of geological lithofacies

In-line inspection solution for codes on complex backgrounds for the plastic container industry

COVID-DenseNet: a deep learning architecture to detect COVID-19 from chest radiology images. Preprints

Truncated inception net: COVID-19 outbreak screening using chest X-rays

Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images

A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization

A novel and efficient deep learning approach for COVID-19 detection using X-ray imaging modality