February 2020 (World Health Organization, 2020a) . As of July 2020, the outbreak of 2019 in Wuhan (China), has extended worldwide Zhu et al., 2020) with 12, 286, 264 confirmed COVID-19 cases including 555, 642 deaths in a short period of 6 months (11 July 2020) (World Health Organization, 2020b) , as presented in Fig. 1 . The clinical attributes, of severe COVID-19 epidemic, are bronchopneumonia that causes cough, fever, dyspnea, and subtle respiratory anxiety ailment Li et al., 2020; countries, but some developed countries also struggle to alleviate that limitation (Wetsman, 2020) .
It is observed that most of the COVID-19 cases have common characteristics on radiographic images, such as CT and X-ray, including bilateral, multi-focal, ground-glass opacities with a peripheral or posterior distribution, mainly in the lower lobes, early-and late-stage pulmonary consolidation (Corman et al., 2020; Huang et al., 2020; Singh et al., 2020; Xu et al., 2020) . Those images can be utilized to develop a sensitive CAD tool to detect COVID-19 pneumonia and be considered a screening tool with RT-PRC (Lee et al., 2020) .
Application of machine learning methods for automatic diagnosis in the medical field, via CAD tools, has recently gained popularity by becoming an adjunct tool for the clinicians (Ker et al., 2017; Litjens et al., 2017; Shen et al., 2017) . The deep CNN-based published methods, for automatic coronavirus recognition, are briefly presented in the next subsection. Ozturk et al. (2020) proposed a model, called DarkCovidNet having 17 convolutional layers, for binary classification (COVID vs No-Findings) and multi-class classification (COVID vs No-Findings vs Pneumonia) using chest X-ray images. The author employed the DarkNet (Redmon and Farhadi, 2017) model for the You Only Look Once (YOLO) (Redmon and Farhadi, 2017) real-time coronavirus detection system. A Deep CNN model, called CoroNet, automatically detect COVID-19 infection from chest X-ray images, which was proposed by Khan et al. (2020) . The CoroNet was based on pre-trained Xception architecture (Chollet, 2017) in ImageNet (Deng et al., 2009) , with a dropout layer (Srivastava et al., 2014) and two fully-connected layers. They trained and evaluated their model on different tasks like binary and multi-class recognition. Ghoshal and Tucker (2020) investigated uncertainty of the coronavirus classification report, using the drop-weights-based Bayesian CNN, as the availability of uncertainty-aware Deep Learning (DL) can ensure more extensive adoption of DL in clinical applications. Narin et al. (2020) implemented three different deep CNN models such as ResNet-50 (He et al., 2016a) , InceptionV3 (Szegedy et al., 2017) , and Inception-ResNetV2 (Szegedy et al., 2017) , where they also used transfer learning for the detection of coronavirus and pneumonia infected patient. The authors showed that chest X-ray images and ResNet-50 are the best tools for the detection of COVID-19. Hemdan et al. (2020) proposed a DL framework, called COVIDX-Net, where they experimented on seven different CNN architectures, such as VGG-19 (Simonyan and Zisserman, 2014) , DenseNet-121 (Huang et al., 2017) , ResNetV2 (He et al., 2016b) , InceptionV3, Inception-ResNetV2, Xception, and MobileNetV2 (Howard et al., 2017) . In the end, their results suggested that VGG-19 and DenseNet-201 are better for coronavirus screening systems with X-ray images. Three comparatively shallow networks, such as MobileNetV2, SqueezeNet (LeCun et al., 2010) , and ResNet-18, and five deep networks, such as InceptionV3, ResNet-101, CheXNet (Rajpurkar et al., 2018) , were (Abbas et al., 2020b) for the classification of COVID-19 chest X-ray images, where the authors implemented the DeTraC in two phases. Firstly, they trained, using a gradient descent optimization, the backbone pre-trained CNN model of DeTraC to extract deep local features from each image. Secondly, they used the class-composition layer of DeTraC to refine the final classification of the images. Zhao et al. (2020) developed diagnosis methods based on multi-task learning and self-supervised learning, where the authors proposed an open-source COVID dataset of CT images with a binary class (COVID and NON-COVID). For the classification task, they train DenseNet-169 and ResNet-50, via a pre-trained model on ImageNet weights, with their newly proposed dataset. Hall et al. (2020) explored the usefulness of the chest X-ray images with various deep CNN models for the diagnosis of the COVID-19 disease. The authors employed pre-trained ResNet-50 and VGG-16 (Simonyan and Zisserman, 2014) on ImageNet. Afshar et al. (2020) proposed a CNN model named COVID-CAPS, which was based on the Capsule Networks (CapsNets) for handling the small datasets of coronavirus.
CapsNets are alternative models of CNN, which are capable of capturing spatial information using routing by agreement, through which capsules try to reach a mutual agreement on the existence of the objects. Their proposed COVID-CAPS model had 4 convolutional layers and 3 capsule layers, where batch normalization (Ioffe and Szegedy, 2015) followed the former layers. The authors fine-tuned all the capsule layers, while the conventional layers were frozen with pre-trained weights of ImageNet. Apostolopoulos and Mpesiana (2020) accomplished comprehensive experiments on state-of-the-art CNN models applying transfer learning. In the end, the authors found that VGG-19 outperforms other CNNs for accuracy, while MobileNetv2 outperforms VGG-19 in terms of specificity. He et al. (2020a) built a COVID CT dataset, called China Consortium of Chest CT Image Investigation (CC-CCII), with three classes: novel coronavirus pneumonia, common pneumonia, and healthy controls.
The authors trained 3D DenseNet3D-121 on their proposed CC-CCII dataset, and they experimentally validated that 3D CNNs outperform 2D CNNs in general. Singh et al. (2020) implemented a CNN-based model named multi-objective differential evolutionbased CNN for the classification of COVID-19. They fine-tuned the parameters of the CNN model using a multi-objective fitness function. The differential evolution algorithm was used for the optimization of the multi-objective fitness function. In differential evolution, the model was optimized iteratively using mutation, crossover, and selection operation to determine the best available solution. extracted massive high-dimensional features, using a pre-trained MobileNetV2 architecture, corresponding to six diseases. Finally, they used fully-connected layers to classify those features for the identification of the coronavirus. Farooq and Hafeez (2020) employed ResNet-50 using transfer learning with progressively resizing the input images to 128 × 128 × 3, 224 × 224 × 3, and 229 × 229 × 3 pixels, where the authors also fine-tuned the network at each stage. Ozkaya et al. (2020) extracted deep features using VGG-16, GoogleNet , and ResNet-50 models, which were classified by Support Vector Machine (SVM) (Furey et al., 2000) with linear kernel function. They also applied the t-test method to reduce the feature dimension for reducing the overfitting. Rajaraman et al. (2020) evaluated ImageNet pre-trained CNN models such as VGG-16, VGG-19, InceptionV3, Xception, Inception-ResNetV2, MobileNetV2, DenseNet-201, and NasNet-mobile (Pham et al., 2018) . Then, they optimized the hyperparameters of the CNNs using a randomized grid search method (Bergstra and Bengio, 2012) . In the end, 6 the authors proposed an ensemble of those CNN models for the final coronavirus recognition. Togaçar et al. (2020) restructured the data classes using a fuzzy color technique, where they stacked a structured image with the original images. The authors trained MobileNetV2 and SqueezeNet to extract the deep features, which were then processed using the social mimic optimization method (Balochian and Baloochian, 2019) . After that, selected features were combined and classified using the SVM for the recognition of coronavirus.
The discussions mentioned above, on automated coronavirus recognition systems, show that the deep CNN approaches are commonly employed methods than the different systems that rely on handcrafted features. The former approaches provide auspicious reproducibility of results and amplify the speed of the diagnosis while being end-to-end methods. While many approaches have already been developed and implemented for coronavirus recognition, there is still room for performance improvement for different datasets. However, it is very impractical to guesstimate the amount of depth of the CNN networks and the times of subsampling, when utilizing datasets are small in size. In this article, we propose an end-to-end coronavirus recognition network called CVR-Net, where we ensemble different scaled feature maps from different encoders through fully-connected layers. Such an ensembling allows the network to access different depths and scales of the feature maps of different encoders for generating the final prediction. In our CVR-Net, the newly added depth and subsampling can not degrade the final prediction as their previous depths and subsampling compensate them. To overcome the overfitting and build a generic CVR-Net, with the limited datasets, we apply geometry-based image augmentations and transfer learning on ImageNet (Krizhevsky et al., 2012) . Besides, we also rebalance the imbalanced class distribution, as a massive number of positive coronavirus images are not available yet due to the recent COVID-19 pandemic. We validate our multi-tasking CVR-Net on three different datasets, of two different modalities, such as CT and X-ray, with a different number of classes (see in Table 1 ). We have collected images from different open-sources, such as Kaggle, GitHub, and grand challenges (see in subsection 2.1). To our best knowledge, the proposed CVR-Net has achieved state-of-the-art results on three different datasets, having a different number of classes, while being an end-to-end coronavirus diagnosis system. The rest of the paper is structured accordingly. We explain the proposed framework for the recognition of coronavirus and datasets in section 2. The results and different experiments are reported in section 3. We interpret the obtained results from the proposed CVR-Net in section 4. Finally, we conclude this paper in section 5.
This section presents the materials and methods for conducting this research. Subsection 2.1 briefly describes utilized dataset. The designing of the proposed network (CVR-Net) is explained in subsection 2.2. Finally, subsection 2.3 describes the training protocol of our network and the evaluation metrics.
We train and evaluate our multi-tasking CVR-Net on three datasets, CT and X-ray images, with a different number of classes. We evaluate the proposed network on three different types of tasks, such as: healthy vs. coronavirus (2-class), healthy vs. pneumonia vs. coronavirus (3-class), and healthy vs. bacterial pneumonia vs. viral pneumonia vs. coronavirus (4-class). As COVID-19 is the recent pandemic all over the world, there is still a lack of suitable annotated public datasets as it requires experts for labeling. However, we collected the positive COVID-19 X-ray images from an open-source GitHub 2 repository of Cohen et al. (2020) , the authors compiled the images from various authentic sources (Radiological Society of North America (RSNA), Radiopaedia, etc). We collect the Pneumonia (both bacterial & viral) and healthy chest X-ray images from Kaggle repository "Chest X-Ray Images (Pneumonia)" (Paul Mooney, 2018) . These two datasets are merged for dataset-1, as utilized in CoroNet by Khan et al. (2020) , which has three different tasks, as presented in Table 1 . The utilized dataset-2 is the combination of dataset-1 and additional images 2 https://github.com/ieee8023/covid-chestxray-dataset 8 from another Kaggle repository "Pneumonia sample X-Rays" (Ahmed Ali, 2020), as it was utilized by Togaçar et al. (2020) . Finally, the dataset-3 is collected from COVID-19 grand challenges , which is the CT images of COVID and healthy patients and collected from Tongji Hospital, Wuhan, China. The distribution of all the three datasets is presented in Table 1 , where we assign three different tasks, with a different number of classes, for dataset-1 and single task for the other two datasets. Several example of CT and X-ray images for different classes is presented in Fig. 2 . We have applied a 5-folds cross-validation technique, as presented in Fig. 3 , for the first two datasets to select training, validation, and testing images. The class-distribution of all the datasets, as shown in Table 1 , demonstrates that the images are imbalanced, which makes the classifier to be biased to the particular class having more samples. However, we have employed class rebalancing techniques by penalizing the majority class's loss to build a generic classifier even though datasets are imbalanced. The models were implemented using the Python programming language with different Python and Keras APIs (Chollet, 2015) and the experiments were carried out on a machine running Windows-10 operating system with the following hardware configuration: Intel R Core TM i7-7700 HQ CPU @ 3.60 GHz processor with Install memory (RAM): 32.0 GB and GeForce GTX 1080 GPU with 8 GB memory.
Efficaciously classifications, of medical images, have an essential role in aiding clinical cure and control. For instance, an X-ray investigation for diagnosing pneumonia is the best approach (Organization et al., 2001) , but it needs professional radiologists or experts, which is a rare, costly, and arduous for some regions. The employment of the conventional machine learning algorithms, in medical image classification, began long ago (Yadav and Jadhav, 2019) , which has several disadvantages, such as the poor performance than the practical standard; the implementation of them is quite slow; the extraction and selection of the features are time-consuming and tedious; fluctuate a lot according to various objects (Kermany et al., 2018) . The deep neural networks, notably the CNNs, are comprehensively applied for image classification or recognition, which have earned powerful performance since 2012 (Rawat and Wang, 2017 images, where each of the encoders has five blocks, namely E 1n and E 2n , n = 1, 2, ..., 5, for encoder-1 and encoder-2, respectively. The encoder-1 consists of the residual and convolutional blocks (He et al., 2016a) , as presented in Fig. 5 , where the residual connections allow the information to flow or skip. The residual connections, also known as skip connections, allow gradients to flow through a network directly, without passing through non-linear activation functions and thus avoiding the problem of vanishing gradients in the proposed CVR-Net (He et al., 2016a) . In residual connections, the output of a weight layer series is added to the original input and then passed through the non-linear activation function, as shown in Fig. 5 . However, in encoder-1, 7 × 7 input convolution, followed by max-pooling with the stride of 2, and pool size of 3 × 3, is used before identity and convolutional blocks. where the GAP layer performs an extreme dimensionality reduction to avoid overfitting.
An height × weight × depth dimensional tensor, in GAP, is reduced to a 1 × 1 × depth vector by transferring height × width feature map to a single number, which contributes to the lightweight design of the proposed CVR-Net. Table 2 presents the implementational details of the proposed CVR-Net. We utilize the feature maps E 13 ∼ E 15 from encoder-1 Another positive prospect of the CVR-Net is that during the training, it can be anticipated that if the gradient of one or more branches vanishes, then other branches can recover it as the final gradient is the average of all the individual gradient.
Training Protocol. The preprocessing of the input images is the crucial requirement of the deep CNN models, as images serve as fuel in the learning of those models. However, as a preprocessing, we apply image augmentations, class rebalancing, and resizing. The where such a weighting pays more attention to samples from the underrepresented class.
We estimate the weights of each class by W j = N j /N , where W j , N , and N j are the weight for class j, the total number of samples, and the number of samples in class j, respectively.
As we noticed that most of the images of all the datasets have a 1 : 1 aspect ratio, all images were resized to 224 × 224 pixels using nearest-neighbor interpolation. Additionally, the scarcity of relatively small medical image datasets has been partially overcome by employing a transfer learning Tajbakhsh et al., 2016) where the previously trained model, so-called pre-trained model, is used to initialize the kernels rather than random initialization. We utilize the ImageNet (Krizhevsky et al., 2012) pre-trained weights to initialize the kernels of the proposed CVR-Net. We employ categorical cross-entropy as a loss function and accuracy as a metric for training our CVR-Net for all the datasets. The loss function has been optimized using the Adam (Kingma and Ba, 2014) optimizer with initial learning rate (LR), exponential decay rates (β 1 , β 2 ) as LR = 0.0001, β 1 = 0.9, and β 2 = 0.999 respectively without AMSGrad variant. The initial learning rate is reduced after 12 epochs by 10.0 % if validation loss stops improving.
Evaluation. We use different metrics, such as recall, precision, F1-score, and accuracy, to evaluate our multi-tasking CVR-Net for coronavirus recognition, which are mathematically defined as follows: 
At the beginning of this section, we present the quantitative results for coronavirus recognition, applying the proposed CVR-Net with different datasets having a different number of classes. Finally, in the end, we compare our multi-tasking results with several recent state-of-the-art results, on the same datasets, to validate our proposal. Table 3 , for evaluating the inter-fold variations. Table 3 ). Those results confess that on an average 96.4 %-samples are correctly recognized by the proposed CVR-Net with type-II error and positive predictive value of 3.6 %, and 96.3 %, respectively. It is also noteworthy, from Table 3 , that the inter-18 fold variation is increasing with the decreased performances for all the metrics. The details class-wise results, from the proposed CVR-Net for this task, are presented in a confusion matrix in Table 5 . It shows that the addition of new class (CPN) with earlier two classes Experiment-3. We further break the CPN class into pneumonia bacterial (CPB) and pneumonia viral (CPV) classes. Thus, we have a total of 4 classes in Task-3, where it has 1583-NOR, 2780-CPB, 1493-CPV, and 500-NCP images. We also apply 5-fold cross-validation in this experiment. The proposed CVR-Net, for this experiment (Task-3), produces the recognition results with accuracy, recall, precision, and F1-score of 82.0 %, 82.0 %, 81.6 %, and 81.6 %, respectively, with increased standard deviation comparing two previous experiments (see Table 3 ). Those results, in this Task-3, expose that the CVR-Net recognizes the classes with higher error rates than two previous experiments, where it has a false-negative rate and positive predictive value of 18.0 % and 81.6 %, respectively. It is also noteworthy that the positive predictive value and type-II error have been reduced by the margins of 18.2 % and 17.8 %, respectively, than binary Task-1 (see in Table 3 ), which indicates that additional 17.8 %-NCP samples are recognized as other classes (false negative) in Task-3. However, a further class-wise investigation is given in a confusion matrix, as shown in Table 6 , which exhibits that the CVR-Net fails to recognize the coronavirus in 40 cases. Table 6 shows that Experiment-4 & Experiment-5. In these two experiments, to train and evaluate the proposed CVR-Net, we use dataset-2, with 3-classes (Task-4), and dataset-3, with 2-classes (Task-5), as presented in Table 1 . Table 3 shows that our model has accuracy, recall, precision, and F1-score of 96.1 %, 96.1 %, 96.1 %, and 96.1 %, respectively, for dataset-2 (Task-4), which are 78.0 %, 78.0 %, 78.0 %, and 78.0 %, respectively, for dataset-3 (Task-5).
Those results on dataset-2 and dataset-3 demonstrate that our CVR-Net model recognizes the coronavirus with type-II errors as 3.9 %, and 22.0 %, respectively. Table 7 and Table 8 show the confusion matrix from our proposed CVR-Net utilizing the dataset-2 and dataset-3, respectively. The matrix, as shown in Table 7 , reveals the FN and FP for coronavirus not as high as in the dataset-1 and dataset-2 (see in Table 3) , it is still better as the utilized dataset is very small in size comparing other two datasets (see in Table 1 ).
Results Comparison. Table 9 represents the performance comparison of the proposed CVR-Net with other recent state-of-the-art methods with dataset-1 (Task-1 and Task-2) and
dataset-3 (Task-5). The remaining two other tasks, (Task-3 and Task-4) are not reported in Table 9 , as state-of-the-art methods were not trained and tested on these datasets. To enhance the recognition performance, authors, in several new methods, utilized more the external data to train their networks, which are not publicly available yet. The improvement of the recognition network may not be due to the superiority of the network itself, but the characteristics of the external data, similar to the test datasets. However, we have reported the results of the methods, which were trained and tested on the same datasets, for fairness in comparison. The proposed CVR-Net produces the best recognition results, as presented in Table 9 , for seven out of the nine cases while performing second-best with the winning methods on the other two cases. Firstly, the proposed CVR-Net yields the best results, for Task-1, concerning the accuracy and type-II errors (recall) by beating the second-best stateof-the-art CoroNet with the margins of 0.8 % and 0.4 %, respectively.
Concerning the positive predictive value, CVR-Net, for Task-1, is behind the state-of-the-art
ResNet-50 & InceptionV3 (Narin et al., 2020) by 0.3 %, but it outperforms the third-best CoroNet ) by a 1.4 % margin with respect to the same metric. Secondly, for Task-2, CVR-Net beats the second-best CoroNet by the margins of 1.3 %, and 1.4 % respectively for precision and accuracy. Although, for the same task and type-II errors (recall), our CVR-Net is behind the winner CoroNet by 0.5 %, it outperforms the third-best recall of COVID-Net (Wang and Wong, 2020) by a margin of 3.1 %. Thirdly, for Task-5 and grand challenge dataset, our proposed CVR-Net outperforms all the methods, such as VGG-16 (He et al., 2020b) , ResNet-18 (He et al., 2020b) , EfficientNet-b0 (He et al., 2020b) , and CRNet (He et al., 2020b) , for all the metrics (see in Table 9 ). The above discussions show that the performance of the proposed CVR-Net for coronavirus recognition is praiseworthy for all the utilized datasets.
The COVID-19 pandemic has a disastrous effect on the health and well-being of the global population. Effective and early screening of infected patients is a critical and crucial step to fight against the COVID-19 epidemic, where the examination and investigation of chest radiography images, via any CAD tool, is one of the vital screening approaches. The class-wise results, for all the tasks (Task-1 to Task-5), in Table 4 to The experimental results on dataset-1 from the CVR-Net, in Table 3 , also demonstrated that the recognition results, for Task-1 (2-class), are better than the other two tasks, such as Task-2: 3-class and Task-3: 4-class, with the same number of total training images. Fig. 6 depicts the impact of adding more classes reduces the performance metrics as such a new class increase the intra-class similarity. Fig. 6 demonstrates that CVR-Net has better recognition results in binary case (Task-1), where it can recognize 493-NCP samples as NCP with only 7 samples as false negative (see in Table 4 ). Such a result is because we keep all the normal and pneumonia images as NOR class, where the intra-class similarity, between NOR and NCP classes, in Task-1, is very less. Whenever we break the NOR class into NOR and CPN classes in Task-2 (3-class), the recognition results are lower than the former Task-1 (see in Table 4, Table 5 , and Fig. 6 ). Remarkably, Table 5 well as many NOR and CPN samples are predicted as CPN and NOR, respectively. Such false-negative and false-positive results reveal that CPN class has similarities with both NOR and NCP, which CVR-Net can not recognize due to fewer samples in the training set. Further breaking of the CPN class into pneumonia bacterial (CPB) and pneumonia viral (CPV) classes, highly decreases all the metrics (see in Fig. 6) , where, unfortunately, CVR-Net recognize the coronavirus with 40 false negative out of 500 samples. Table 6 shows that there are many false negative and false positives, which exhibit that with fewer training samples, the addition of more classes introduces the inter-class similarity and intraclass diversity. Those discussions reveal that the coronavirus recognition, from the proposed CVR-Net, is admirable, even with the less training samples, if we use less number of class as it has a minor inter-class similarity and intra-class diversity. In the future, the addition of more distinctive samples in every class, in Task-2 and Task-3, can lead CVR-Net to perform markedly even for the increased number of classes, as it has a better design to avoid the overfitting and vanishing gradient problems.
The number of people infected by COVID-19 is increasing day-by-day, which can permanently damage the lungs and later provoke death. During this pandemic emergency, many countries struggle with their shortage of resources for proper recognition of the coronavirus, where such recognition, with negligible false negative, is highly essential. This article aimed to design an artificial system for automated distinguishing people with positive coronavirus. With this thing in mind, we have proposed and implemented an end-to-end deep learning-based model, called CVR-Net, to recognize the coronavirus with the very less false negative from chest radiography images without any intermediate intervention. The multiscale-multi-encoder design of the CVR-Net ensures robustness in recognition, as the final prediction probability is the aggregation of multiple scales and encoders. In the proposed CVR-Net, different integral parts of the proposed preprocessing, such as image augmentations, class rebalancing, and transfer learning, boosted the performance of coronavirus recognition. The class rebalancing protects the model from being biased to a particular overrepresent class, as a massive number of manually annotated positive images are not publicly available yet. The performance can further be increased by precisely segmenting the lung and adding more distinctive training samples. We also intend to deploy our trained CVR-Net to a web application for clinical utilization.