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Diagnosing COVID-19 From Chest CT Scan Images Using Deep Learning Models

A novel coronavirus named COVID-19 has spread speedily and has triggered a worldwide outbreak of respiratory illness. Early diagnosis is always crucial for pandemic control. Compared to RT-PCR, chest computed tomography (CT) imaging is the more consistent, concrete, and prompt method to identify COVID-19 patients. For clinical diagnostics, the information received from computed tomography scans is critical. So there is a need to develop an image analysis technique for detecting viral epidemics from computed tomography scan pictures. Using DenseNet, ResNet, CapsNet, and 3D-ConvNet, four deep machine learning-based architectures have been proposed for COVID-19 diagnosis from chest computed tomography scans. From the experimental results, it is found that all the architectures are providing effective accuracy, of which the COVID-DNet model has reached the highest accuracy of 99%. Proposed architectures are accessible at https://github.com/shamiktiwari/ CTscanCovi19 and can be utilized to support radiologists and reserachers in validating their initial screening.


INTRODUCTION
A global pandemic has been resulting from the COVID − 19 virus, which was detected in November 2019. This virus shares genetic similarities with the SARS virus, which was discovered in 2002. This virus was created in bats and then transmitted to humans through them (Jahangir et al., 2020). This virus can spread via the air through water droplets released by affected people while sneezing, speaking, or coughing, and can be active for up to three hours (Alpaydin et al., 2021). The virus is also capable of surviving on the surfaces of objects for a very long time (Bhardwaj et al., 2021).  Figure 1. The details of these architectures are discussed here.

Transfer Learning
An inherent benefit of the ConvNet model is that the performance of the model improves with an intensification in the quantity of training images. So, designing deep machine learning-based architectures needs a huge volume of categorized data. However, an adequate amount of data is not available in several medical-related image classification applications. The concept of transfer learning is quite effective for handling such scenarios. The idea of transfer learning is to transfer the learning gained in one domain to another domain. In the context of ConvNet, the concept of transfer learning can be applied by training parameters on one dataset and utilizing them on another dataset. In a more detailed manner, it can be stated that the first layers of a trained ConvNet model can be passed to the first layers of the second model. The other layers of the second model can be initialized randomly and trained as per the problem. After this, for the training of the network through the new dataset, either we fine-tune more layers or we allow parameters from the dense layer of the model to be adjusted. Usually, we keep the first ConvNet layer fixed as its function is edge extraction, and it is done in all problems. As we no longer train all parameters every time, the concept of transfer learning is useful in medical image-related problems that have a small labeled dataset. As shown in Figures

Capsule Network (CapsNet)
Capsule Network, introduced in 2017 by (Sabour et al., 2017). It has offered a shift in paradigm in neural computation. In CapsNet, the traditional approach of scalar neural computation has been replaced by the vectorized approach. It arranges the neurons into clusters called capsules to efficiently learn and characterize the patterns in various object recognition applications. The capsule in CapsNet characterizes a vector to show the likelihood of the entity's existence, and the orientation denotes the entity's attributes (Deng et al., 2018). This squash function's definition is as given in equation 1: where as illustrated in equation 2, w j is the weighted aggregate output of capsules: and the affine transformation defined in equation 3 is denoted by ˆ| v j i : The architecture of capsules in CapsNet is shown in Figure 4.

3-D Convolution Neural Network
3D convolution calculates low-level feature representations by applying a three-dimensional filter to the dataset that moves in the x, y, and z directions (Zou et al., 2017, Alakwaa et al., 2017. The shape of the output is identical to the cube. They are useful in the identification of events in videos and three-dimensional medical images, etc. They are not limited to three-dimensional space but can also be applied to two-dimensional space inputs. Convolution of a 3D mask is applied to perform 3D convolution. Mathematically, the response at a position on the feature-map in the layer is specified by equation where C i is the 3-D filter size, A i and B i denote the height and width of the 3D convolution filter respectively. v ij xyz is the response at the j th feature cube of the i th layer at the location x y z , , valuable to gradient descent and backpropagation and evades the gradient disappearance problem. Figure 5 depicts the 3D ConvNet's basic design

SIMULATION ENVIRONMENT AND EXPERIMENTS
In this section, we explore the chest computed tomography image dataset and experiment with all four deep machine learning architectures as discussed in the previous section.

Dataset
A dataset of chest computed tomography scans from clinics in Sao Paulo, Brazil is taken. The total computed tomography scan is 2482, where 1252 scans are of COVID − 19 affected patients, while 1230 scans are of non COVID − 19 affected patients who have other respiratory diseases (Soares et al., 2020). Sample scans from this dataset are presented in Figure 6. It is divided into 3 subsets: train, validation, and test, with 1737, 524, and 221 scans in each subset, respectively.

Experiment 1: Covid-RNet Model
In this experiment, the ResNet-50 model as discussed in the methodology section is applied and termed as Covid-RNet. The top-most layers (that is, fully connected and the classification layers) were removed. One convolution layer, two new dense layers, and a classification layer with softmax activation were applied to replace the removed layers. These newly added layers are trained utilizing features extracted from the transferred learned layers of the pre-trained ResNet-50 model. Here, transferred layers are frozen to only extract image patterns without adjusting their weights. The ResNet50 receives scans of pixel size 224*224. So, all the scans are resized to 224*224. The optimizer applied for this model is Adam. Categorical-cross entropy is applied as the loss function to calculate the loss for the Covid-RNet model. Figure 7 presents the accuracy and loss plots for the training of the Covid-RNet model. If the graph of training loss reduces to a point of stabilization, the graph of

Figure 7. Progress of accuracy (a) and loss (b) for the Covid-RNet model's training. Accuracy and Loss change sharply for the first twenty steps and stabilize after twenty steps.
learning plots reveals a satisfactory fit. The validation loss graph reaches a point of stabilization, with a narrow opening between it and the training loss. Results are provided in Table 2.

Experiment 2: Covid-CapsNet Model
As noted in the methodology section, the CapsNet model architecture is applied for COVID − 19 identification, termed as Covid-CapsNet. The model is assembled with the Adam optimizer and categorical cross-entropy as a loss function. The initial learning rate is .0012 and a total of 25 iterations with a batch size of 64 is applied. Figure 8 presents the accuracy and loss plots for the training of the Covid-CapsNet model. All the computed tomography-scans are resized to 64*64 to train and test the model. Results are offered in Table 2.

Experiment 3: Covid-DNet Model
In this experiment, the DenseNet-121 model is applied and termed as Covid-DNet. The top-most layers (that is, fully connected and the classification layers) are removed. One convolution layer, two new dense layers, and a classification layer with softmax activation are applied to replace the removed layers. These newly added layers are trained utilizing features extracted from the transferred learned layers of the pre-trained DenseNet-121 model. Here, transferred layers are frozen to only extract image patterns without adjusting their weights. All scans are resized to the size of 224*224. The optimizer applied for this model is Adam. The initial learning rate is.0015 and 50 iterations with a  batch size of 64 are applied. Figure 9 displays the accuracy and loss plots of the Covid-DNet model. Results are offered in Table 3.

Experiment 4: Covid-3DConvNet Model
In this experiment, the 3D ConvNet model is designed for COVID − 19 identification, termed Covid-3DConvNet. The 3D-ConvNet model has 4 convolutional layers, with 2 max-pooling layers followed by 4 dense layers. A 2*2*2 max-pooling is applied to the output of the second and fourth convolutional layers. The first three dense layers consist of 2048, 512, and 512 nodes in that order. The output of the last dense layer with 2 nodes is fed to a softmax layer for classification output. Rectified Linear Input (ReLu) is applied as the activation function for the convolution output. Dropout with a rate of 0.4 is performed after the first three dense layers. Dropout is a technique applied to prevent a model from overfitting. A learning rate parameter of 0.00015 is applied. The values of β1, β2, and epsilon are 0.9, 0.999, and 0.1 respectively. The 3D-ConvNet model is trained for 100 iterations with a batch size of 32. For training and testing with the Covid-3DConvNet model, all the scans of size 128*128 are resized to 32*32*16 for training and testing. The training and validation accuracy and loss plots are plotted in Figure 10. The performance metrics are offered in Table 3.

RESULTS AND DISCUSSION
To evaluate the performance of architectures, metrics like accuracy (A), precision (P), sensitivity, and F-score (F) are applied. Accuracy is measured as the proportion of correctly classified cases. When the classes are extremely imbalanced, the precision score is a good indicator of prediction success. It denotes the proportion of genuine positives to the total number of true and false positives. The number of positive class correct predictions out of all the positive examples in the computed tomography-scan image dataset is measured by the recall. An F-Measure offers a single value that accounts for both recall and precision measures in a one value. Table 2 and Table 3 show the findings in terms of the aforementioned performance measures. It can be concluded from these tables that all of the deep machine learning architectures provide effective accuracies. The Covid-RNet, Covid-CapsNet, Covid-DNet, and Covid-3DConvNet architectures provide 0.98, 0.94, 0.99, and 0.97 accuracies, respectively. The graphs like Receiver-operating characteristic (ROC) and Area Under the Curve (AUC) are applied for more accurate analysis of the projected architectures. These plots show the ability of the classifier to differentiate different classes (Tiwari, 2020;Tiwari and Jain, 2022). Results shown in Figure 11 show the superiority of the Covid-RNet and Covid-DNet architectures over the Covid-CapsNet and Covid-3DConvNet architectures.
The performance measures are compared in Figure 12. The precision, sensitivity, F-score, and average accuracy are all 0.99 for the Covid-DNet model, which is the highest among all the Covid identification architectures. It confirms that the Covid-DNet model, which utilizes the DensNet-121 model as a pre-trained model as transfer learning, is the best among all the architectures.

CONCLUSION AND FUTURE SCOPE
As of June 6, 2021, globally, 177,419,783 humans have been affected by the COVID − 19 virus and 3,838,670 have lost their lives. This has happened due to fewer testing facilities. To address this issue with current resources, the authors have designed a system that uses chest computed tomography-scan pictures to diagnose COVID − 19 infection. Four deep machine learning-based architectures have been designed utilizing DNet, RNet, CapsNet, and 3D ConvNet. Architectures are trained and tested on an online chest computed tomography-scan image dataset. Their performance is matched on the scale of accuracy, precision, sensitivity, F-score, and AUC. It is found that the model designed utilizing DNet had the highest accuracy of 99%. This model is one step in the direction of diagnosing COVID − 19 affected patients by utilizing an easily available radiography structure. Moreover, it will give a prompt result at a very cheap rate. This can help healthcare workers in their fight against this pandemic. Therefore, it can be concluded that the chest computed tomography scan diagnosis utilizing deep machine learning architectures

and normal classes (a) ROC/AUC plot for Covid-RNet Classification model, (b) ROC/AUC plot for Covid-CapsNet model, (c) ROC/ AUC plot for Covid-DNet Classification model, (d) ROC/AUC plot for Covid-3DConvNet Classification model
is sensitive to COVID − 19 identification. As a future scope, the authors have planned to combine chest computed tomography scan results with some other clinical tests to get a more accurate prediction of COVID − 19 .

FUNDING AGENCy
Publisher has waived the Open Access publishing fee.