# Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains

Authors: Yousef Alhwaiti; Muhammad Hameed Siddiqi; Madallah Alruwaili; Ibrahim Alrashdi; Saad Alanazi; Muhammad Hasan Jamal
Journal: Complexity
 (2021)

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## Abstract

Many countries are severely affected by COVID-19, and various casualties have been reported. Most countries have implemented full and partial lockdowns to control COVID-19. Paramedical employee infections are always a threatening discovery. Front-line paramedical employees might initially be at risk when observing and treating patients, who can contaminate them through respiratory secretions. If proper preventive measures are absent, front-line paramedical workers will be in danger of contamination and can become unintentional carriers to patients admitted in the hospital for other illnesses and treatments. Moreover, every country has limited testing capacity; therefore, a system is required which helps the doctor to directly check and analyze the patients’ blood structure. This study proposes a generalized adaptive deep learning model that helps the front-line paramedical employees to easily detect COVID-19 in different radiology domains. In this work, we designed a model using convolutional neural network in order to detect COVID-19 from X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) images. The proposed model has 27 layers (input, convolutional, max-pooling, dropout, flatten, dense, and output layers), which has been tested and validated on various radiology domains such as X-ray, CT, and MRI. For experiments, we utilized 70% of the dataset for training and 30% for testing against each dataset. The weighted average accuracies for the proposed model are 94%, 85%, and 86% on X-ray, CT, and MRI, respectively. The experiments show the significance of the model against state-of-the-art works.


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## Body

## 1. Introduction

The rapid spread of COVID-19 has motivated scientists to quickly develop countermeasures using technologies such as cognitive computing, deep learning, artificial intelligence, machine intelligence, cloud-based collaboration, and wireless communication [1].Cognitive computing simulates human thought processes and is extensively used in fields such as finance and investment, healthcare and veterinary medicine, travel, and mobile systems [2–4]. The Internet of things (IoT) is implemented on interconnected electronic devices with unique identifiers (UIDs), such as computers, smartphones, coffeemakers, washing machines, and wearable devices [5, 6]. The IoT, along with cloud computing, Artificial Intelligence (AI), Machine Learning (ML), and deep learning, could be a powerful tool to combat COVID-19 [7, 8], and 4th generation (4G) and 5th generation (5G) wireless communication technologies have the potential to revolutionize many sectors, including healthcare [9–11]. China has already used 5G technology to fight the COVID-19 pandemic by monitoring patients, collecting and analyzing data, and tracking viruses [1].Most developing countries utilize wireless technologies, laboratory-based trails, and radiological investigations in order to recognize and diagnose COVID-19 [12]. A standard method is real-time reverse transcription polymerase chain reaction (qRT-PCR), but false-negative results can occur due to asymptomatic patients, and mistakes may also affect its role in identifying COVID-19 [13, 14]. In the early stages, imaging technologies such as CT scan, MRI, and X-rays might play a vital role in detecting COVID-19 patients [15–17].Radiology-based chest scanning has been employed to investigate pneumonia [18]. An artificial intelligence- (AI-) based tool was developed [19] to automatically detect, quantify, and monitor COVID-19 and to differentiate affected and normal patients. A deep learning-based approach [20] was developed to automatically segment the entire lung with infection sites under a chest CT. Similarly, an early screening system based on deep learning can discriminate influenza (viral pneumonia) from vigorous cases and COVID-19 [21]. A deep learning-based approach can extract graphical features from CT images of COVID-19 [22]. These features deliver prior medical analysis pathogenic testing and have been claimed to save crucial time for disease investigation. However, most consider just one radiology domain, such as X-ray or CT.This work builds a deep learning approach in order to notice COVID-19 from various radiological input images such as X-ray, CT, and MRI. The model is a convolutional neural network (CNN) whose 27 layers include input, convolutional, max-pooling, dropout, flatten, dense, and output. The input layer accepts input grayscale images of size 128 × 128 and uses 64 filters of size 3 × 3. Ae ReLU activation function is employed in the input layer and all hidden layers. Following the max-pooling layer is a dropout layer to avoid overfitting. This drops out different neurons in the hidden layer. The percentage of neurons to drop should be specified when using the dropout function. We drop 30%. Next are two convolutional layers, both with 128 filters of size 3 × 3, then a 2 × 2 max-pooling layer, and a dropout layer to drop 30% of neurons. We add three convolutional layers with 256 filters, each with size 3 × 3, followed by a max-pooling layer with the same parameters as the previous pooling layer. We also drop 30% of the output neurons. We continue increasing filters in more layers, adding three convolutional layers with 512 filters in each layer, with the same filter size as previous layers. A max-pooling layer follows this stack of layers, and 30% of the neurons are dropped. Two stacks, the same as previous layers, are added using the same parameters.The rest of the paper is organized as follows. Section2 summarizes state-of-the-art work in various radiology domains. Section 3 presents the proposed methodology. The datasets used in this research are described in Section 4. The experimental environment for the proposed approach is presented in Section 5, and the results are discussed in Section 6. Section 7 discusses conclusions and directions of future work.


## 2. Related Works

Various radiology techniques (e.g., X-ray, CT, and MRI) have been utilized as imaging modalities in the diagnosis of COVID-19, and research has proposed the identification of COVID-19 against different radiology methods, with various limitations.An early-stage screening model [23] could differentiate COVID-19 patients from normal humans by employing deep learning techniques under pulmonary CT images, with 86.7% accuracy against 618 CT samples. However, the segmentation model employed before feeding it to the learning model could lose some important features and cause misclassification, and only limited radiology images were employed in experiments. An automatic deep CNN system [18] was based on pretrained models under chest X-ray images. This heuristic model utilized limited X-ray images in a controlled domain.An integrated technique based on an artificial neural network and convolutional CapsNet [24] was developed to identify COVID-19 against chest X-ray images with pill networks. The performance was assessed with binary and multiclass classifications such as infected, normal, and pneumonia, indicating a 97% recognition rate on binary classification and 84% on multiclass classification. There was no rule to find the structure of the artificial neural network, which had no specific scheme to define the structure of neurons, which could be achieved by experience or trial-and-error [25]. When training of the neural network was completed, the network was reduced to a specific value of error on image samples; hence, it provided no optimum outcomes [25].Some recent systems [18, 26–28] have utilized deep learning and artificial intelligence to identify COVID-19, but only on X-ray images. Similarly, a commercial platform was used to classify infected patients and normal humans [29], with limited contribution from the authors, who utilized only X-rays in experiments. Deep learning and CNN were used to classify positive patients with coronavirus and healthy patients [30]. A very small dataset of X-ray images was used, which might not be applicable in naturalistic domains.An automated method [31] was proposed to detect COVID-19-positive patients from normal humans, employing a deep learning-based network coupled with gradient-weighted class activation mapping (Grad-CAM) for feature extraction under CT scan images. However, Grad-CAM-based methods require modification of the network architecture, which could degrade accuracy; computational Grad-CAM is expensive [32]; and a nonstandard dataset was utilized. A deep CNN, called decompose, transfer, and compose (DeTraC), was used with principal component analysis (PCA) as a feature dimension reduction method to identify coronavirus against chest X-ray images [33]. However, PCA is problematic in the precise assessment of the covariance matrix [34]. Moreover, even modest invariance might not be taken by PCA unless the training data openly deliver this evidence [35]. A deep learning-based system to identify COVID-19 from normal humans had a recognition rate of up to 100% [36], which is not realistic. Only one radiology (X-ray) image was utilized. Similarly, a system was proposed to classify infected, normal, and pneumonia cases with significant accuracy [37], and a deep CNN-based system was proposed to identify patients with coronavirus and normal humans [38]. However, both systems utilized limited X-ray images and used one radiology images.We develop a deep learning model to accurately categorize the infected patients with COVID-19 and normal humans. The model employs radiology input images such as X-ray, CT, and MRI, through which we can prove the robustness of the model, which is based on 27 layers of a CNN, including input, convolutional, max-pooling, dropout, flatten, dense, and output layers, and shows significant performance on various radiology images such as X-ray, CT scan, and MRI, compared to state-of-the-art methods.


## 3. Materials and Methods

We describe the proposed deep learning-based approach, whose flowchart against X-ray images is shown in Figure1.Figure 1
Flow diagram of the proposed approach against X-ray images.The model is based on a Convolutional Neural Network (CNN) with 27 layers. The input layer accepts a grayscale image of size 128 × 128 and uses 64 filters of size 3 × 3. A rectified linear unit (ReLU) activation function is used in the input layer and all hidden layers, where ReLU is defined by the relationR(z) = max(0, z), as shown in Figure 2.Figure 2
ReLU activation function [39].The ReLU activation function omits negative pixels in the input image. The second layer is a convolutional layer that has 64 filters of size 3 × 3. Next is a max-pooling layer that takes the maximum value for each patch of the feature map, with pool size and stride both 2 × 2.Next is a dropout layer to avoid overfitting by dropping out different neurons in hidden layers. We drop 30% of the neurons to reduce overfitting. The dropout technique is shown in Figure3.Figure 3
Dropout technique [40].The output of the previous layer is flattened to convert a matrix to a single layer. For instance, an output shape of (1, 128, 18) is flattened to (1, 16384). Then, two dense layers with 4096 units each are added. An ReLU activation function is used in both layers. The last layer is the output layer with two neurons, which is the number of classes (COVID-19 positive and COVID-19 negative). A soft-max activation function normalizes the input vector from the previous layer of real numbers to a probability distribution(1)σZezj∑j=1Kezj,fori=1,…,KandZ=z1,…,zk∈RK.The proposed approach is described in Figure4.Figure 4
(a), (b) The workflow of the proposed model.
(a)(b)


## 4. Datasets Used

We utilized the following datasets to show the efficacy of the developed approach.


### 4.1. X-Ray Image Dataset

We utilized a radiology dataset with 270 X-ray images from males and females of age 20–55 years, collected from various open sources (used to diagnose coronavirus). During implementation, we regularly updated the dataset to incorporate the latest complex chest X-ray images. The dataset was thoroughly checked by medical experts (physicians). We did not provide metadata for patients. Images were converted to a vector with dimensions 1 × 6400 by decreasing the dimension of every input image to 80 × 80. To avoid imbalance, we utilized 135 normal patients’ images and 135 COVID-19-positive images. The dataset was collected over a period of 3 months (June to August 2020).


### 4.2. Computed Tomography (CT) Scan Image Dataset

The CT image dataset contained 270 chest CT images. The dataset was built from open sources commonly used to diagnose COVID-19. The dataset incorporated new complex CT scan images that were systematically checked by doctors. The images were from males and females of age 35 to 55 years. For experiments, images in this dataset were transformed to a vector with dimensions 1 × 6400 by decreasing the dimension of every input image to 80 × 80. To avoid imbalance, we utilized images of 135 normal patients and 135 images from COVID-19-positive patients. The dataset was collected over 3 months (June to August 2020).


### 4.3. Magnetic Resonance Imaging (MRI) Image Dataset

Another type of radiology dataset was of MRI scans, which generate two types of images. T1-weighted images highlight (brighten) lipids and fats by a radio-frequency pulse sequence, and T2-weighted images highlight also water. So, the timing of the radiofrequency pulse sequence highlights the target tissues. We included 270 MRI images of males and females of age 35 to 60 years. These were confirmed cases of COVID-19. We added controls with approximately similar ages and genders but without COVID-19. All images were converted to a vector of dimension 1 × 6400 by decreasing the dimension of every input image to 80 × 80. To avoid imbalance, we utilized 135 images of normal patients and 135 COVID-19-positive images. The dataset was collected over 3 months (June to August 2020).


## 4.1. X-Ray Image Dataset

We utilized a radiology dataset with 270 X-ray images from males and females of age 20–55 years, collected from various open sources (used to diagnose coronavirus). During implementation, we regularly updated the dataset to incorporate the latest complex chest X-ray images. The dataset was thoroughly checked by medical experts (physicians). We did not provide metadata for patients. Images were converted to a vector with dimensions 1 × 6400 by decreasing the dimension of every input image to 80 × 80. To avoid imbalance, we utilized 135 normal patients’ images and 135 COVID-19-positive images. The dataset was collected over a period of 3 months (June to August 2020).


## 4.2. Computed Tomography (CT) Scan Image Dataset

The CT image dataset contained 270 chest CT images. The dataset was built from open sources commonly used to diagnose COVID-19. The dataset incorporated new complex CT scan images that were systematically checked by doctors. The images were from males and females of age 35 to 55 years. For experiments, images in this dataset were transformed to a vector with dimensions 1 × 6400 by decreasing the dimension of every input image to 80 × 80. To avoid imbalance, we utilized images of 135 normal patients and 135 images from COVID-19-positive patients. The dataset was collected over 3 months (June to August 2020).


## 4.3. Magnetic Resonance Imaging (MRI) Image Dataset

Another type of radiology dataset was of MRI scans, which generate two types of images. T1-weighted images highlight (brighten) lipids and fats by a radio-frequency pulse sequence, and T2-weighted images highlight also water. So, the timing of the radiofrequency pulse sequence highlights the target tissues. We included 270 MRI images of males and females of age 35 to 60 years. These were confirmed cases of COVID-19. We added controls with approximately similar ages and genders but without COVID-19. All images were converted to a vector of dimension 1 × 6400 by decreasing the dimension of every input image to 80 × 80. To avoid imbalance, we utilized 135 images of normal patients and 135 COVID-19-positive images. The dataset was collected over 3 months (June to August 2020).


## 5. Experimental Setup

We performed many experiments to show the significance of the proposed model against each dataset; these were divided into 70% for training and 30% for testing for all tested algorithms. The same model architecture was used for each dataset, with different hyperparameters.All experiments were performed using Python, TensorFlow, and Google Colab (for training) on an Intel Pentium Core i7-6700 (3.4 GHz) with 16 GB RAM. Experiments are described as follows:(i)
The first experiment assessed the proposed model against chest X-ray, CT scan, and MRI datasets through an average cross-validation scheme.(ii)
The second experiment included a set of subexperiments performed under the absence of the developed approach against all three datasets. We utilized logistic regression, support vector machine, random forest, k-nearest neighbor, artificial neural network, Naive Bayes, decision tree, passive aggressive classifier, multilayer perceptron, and extra tree classifiers.(iii)
The third experiment compared the proposed technique to the state of the art.


## 6. Results and Discussion

### 6.1. First Experiment

The results of the first experiment are shown in Table1.Table 1
Recognition rates of the proposed approach against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified94%Misclassified6%CT scanClassified85%Misclassified15%MRIClassified86%Misclassified16%As can be seen from Table1, the model got significant accuracy among state of the art. It trained in different datasets with different hyperparameters using same structure of the model. That indicates that the structure of the model is significant. Also, the model was compared with different machine learning algorithms to show the difference in accuracy between deep learning and regular machine learning algorithms.


### 6.2. Second Experiment

The results of the second experiment are presented in Tables2–11.Table 2
Recognition rates of logistic regression (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified73%Misclassified27%CT scanClassified65%Misclassified35%MRIClassified74%Misclassified26%Table 3
Recognition rates of support vector machine (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified75%Misclassified25%CT scanClassified51%Misclassified49%MRIClassified78%Misclassified22%Table 4
Recognition rates of random forest (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified79%Misclassified21%CT scanClassified74%Misclassified26%MRIClassified77%Misclassified23%Table 5
Recognition rates of k-nearest neighbor (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified79%Misclassified21%CT scanClassified69%Misclassified31%MRIClassified81%Misclassified19%Table 6
Recognition rates of support vector machine (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified72%Misclassified28%CT scanClassified66%Misclassified34%MRIClassified79%Misclassified21%Table 7
Recognition rates of Naïve Bayes (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified80%Misclassified20%CT scanClassified58%Misclassified42%MRIClassified77%Misclassified23%Table 8
Recognition rates of decision tree (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified72%Misclassified28%CT scanClassified65%Misclassified35%MRIClassified77%Misclassified23%Table 9
Recognition rates of passive aggressive classifier (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified65%Misclassified35%CT scanClassified71%Misclassified29%MRIClassified70%Misclassified30%Table 10
Recognition rates of multilayer perceptron (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified60%Misclassified40%CT scanClassified66%Misclassified34%MRIClassified47%Misclassified53%Table 11
Recognition rates of extra tree classifier (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified81%Misclassified19%CT scanClassified79%Misclassified21%MRIClassified79%Misclassified21%We observe from Tables2–11 that all of the existing classifiers did not achieve better accuracy against the three datasets. This is because most of the medical images are sensitive to noise and other environmental factors. The proposed approach can achieve significant accuracy under the presence of noise and other environmental factors, as shown in Table 1.


### 6.3. Third Experiment

The recognition rates of the proposed model and other models are shown in Tables12–14, which present that the proposed model achieved higher accuracy on all three datasets.Table 12
The comparison of the proposed approach along with the state-of-the-art methods on the X-ray dataset (unit: %).
State of the artWeighted average recognition ratesStandard deviation[28]89.6±2.9[30]83.5±3.7[41]80.3±2.6[42]85.4±1.2[43]79.8±3.8[44]89.3±2.3[45]91.5±1.9[46]90.5±2.7Proposed model94.0±3.5Table 13
The comparison of the proposed approach along with the state-of-the-art methods on the CT scan dataset (unit: %).
State of the artWeighted average recognition ratesStandard deviation[28]82.3±1.7[47]75.2±1.9[48]84.7±2.1[49]82.4±2.5[50]83.4±1.2[51]79.2±3.6Proposed model85.0±3.5Table 14
The comparison of the proposed approach along with the state-of-the-art methods on the MRI dataset (unit: %).
State of the artWeighted average recognition ratesStandard deviationLogistic regression74.0±3.1Support vector machine78.0±1.8Random forest77.0±2.4k-nearest neighbor81.0±1.1Artificial neural network79.0±3.7Naïve Bayes71.0±5.2Decision tree77.0±2.6Passive aggressive classifier70.0±4.6Multilayer perceptron47.0±6.1Extra tree classifier79.0±4.4Proposed model86.0±3.5As illustrated in Tables12–14, the proposed approach achieved higher accuracy than other recent works under all the three radiology datasets.


### 6.4. Discussion

COVID-19 has affected millions of people around the world. The early detection of COVID-19 could help in stopping the spread. One of the most effective detections is screening the infected patients. Deep learning plays an affective role in this detection, and it is more accurate. There are many studies implemented in to detect COVID-19 using deep learning. In this research, we have used a deep learning neural network (convolutional neural network) to detect COVID-19 from different radiologists. Our model results are comparable to the state of the art because we have built a robust model. What made our model a comparable result is that we build the model using different layers (27 layers), adjusting hyperparameters, using effective percentage in dropout, size of the filters in convolutional and pool layers, and using a suitable way in preprocessing. These layers include input, convolutional, max-pooling, dropout, flatten, dense, and output. The input layer accepts input grayscale images of size 128 × 128 and uses 64 filters of size 3 × 3. An ReLU activation function is used in the input layer and all hidden layers. Following the max-pooling layer is a dropout layer to avoid overfitting. This drops out different neurons in the hidden layer. The percentage of neurons to drop should be specified when using the dropout function. We drop 30%. Next are two convolutional layers, both with 128 filters of size 3 × 3, then a 2 × 2 max-pooling layer, and a dropout layer to drop 30% of neurons. We add three convolutional layers with 256 filters, each with size 3 × 3, and then a max-pooling layer with the same parameters as the previous pooling layer. We also drop 30% of the output neurons. We continue increasing filters in more layers, adding three convolutional layers with 512 filters in each layer, with the same filter size as previous layers. A max-pooling layer follows this stack of layers, and 30% of the neurons are dropped. Two stacks, the same as previous layers, are added using the same parameters. Moreover, the model got significant accuracy among the state of the art. It trained in different datasets with different hyperparameters using same structure of the model. That indicates that the structure of the model is significant. Also, the model was compared with different machine learning techniques to show the difference in accuracy between deep learning and regular machine learning algorithms.


## 6.1. First Experiment

The results of the first experiment are shown in Table1.Table 1
Recognition rates of the proposed approach against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified94%Misclassified6%CT scanClassified85%Misclassified15%MRIClassified86%Misclassified16%As can be seen from Table1, the model got significant accuracy among state of the art. It trained in different datasets with different hyperparameters using same structure of the model. That indicates that the structure of the model is significant. Also, the model was compared with different machine learning algorithms to show the difference in accuracy between deep learning and regular machine learning algorithms.


## 6.2. Second Experiment

The results of the second experiment are presented in Tables2–11.Table 2
Recognition rates of logistic regression (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified73%Misclassified27%CT scanClassified65%Misclassified35%MRIClassified74%Misclassified26%Table 3
Recognition rates of support vector machine (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified75%Misclassified25%CT scanClassified51%Misclassified49%MRIClassified78%Misclassified22%Table 4
Recognition rates of random forest (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified79%Misclassified21%CT scanClassified74%Misclassified26%MRIClassified77%Misclassified23%Table 5
Recognition rates of k-nearest neighbor (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified79%Misclassified21%CT scanClassified69%Misclassified31%MRIClassified81%Misclassified19%Table 6
Recognition rates of support vector machine (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified72%Misclassified28%CT scanClassified66%Misclassified34%MRIClassified79%Misclassified21%Table 7
Recognition rates of Naïve Bayes (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified80%Misclassified20%CT scanClassified58%Misclassified42%MRIClassified77%Misclassified23%Table 8
Recognition rates of decision tree (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified72%Misclassified28%CT scanClassified65%Misclassified35%MRIClassified77%Misclassified23%Table 9
Recognition rates of passive aggressive classifier (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified65%Misclassified35%CT scanClassified71%Misclassified29%MRIClassified70%Misclassified30%Table 10
Recognition rates of multilayer perceptron (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified60%Misclassified40%CT scanClassified66%Misclassified34%MRIClassified47%Misclassified53%Table 11
Recognition rates of extra tree classifier (under the absence of the proposed approach) against X-ray, CT, and MRI datasets.
DatasetsRecognition ratesX-rayClassified81%Misclassified19%CT scanClassified79%Misclassified21%MRIClassified79%Misclassified21%We observe from Tables2–11 that all of the existing classifiers did not achieve better accuracy against the three datasets. This is because most of the medical images are sensitive to noise and other environmental factors. The proposed approach can achieve significant accuracy under the presence of noise and other environmental factors, as shown in Table 1.


## 6.3. Third Experiment

The recognition rates of the proposed model and other models are shown in Tables12–14, which present that the proposed model achieved higher accuracy on all three datasets.Table 12
The comparison of the proposed approach along with the state-of-the-art methods on the X-ray dataset (unit: %).
State of the artWeighted average recognition ratesStandard deviation[28]89.6±2.9[30]83.5±3.7[41]80.3±2.6[42]85.4±1.2[43]79.8±3.8[44]89.3±2.3[45]91.5±1.9[46]90.5±2.7Proposed model94.0±3.5Table 13
The comparison of the proposed approach along with the state-of-the-art methods on the CT scan dataset (unit: %).
State of the artWeighted average recognition ratesStandard deviation[28]82.3±1.7[47]75.2±1.9[48]84.7±2.1[49]82.4±2.5[50]83.4±1.2[51]79.2±3.6Proposed model85.0±3.5Table 14
The comparison of the proposed approach along with the state-of-the-art methods on the MRI dataset (unit: %).
State of the artWeighted average recognition ratesStandard deviationLogistic regression74.0±3.1Support vector machine78.0±1.8Random forest77.0±2.4k-nearest neighbor81.0±1.1Artificial neural network79.0±3.7Naïve Bayes71.0±5.2Decision tree77.0±2.6Passive aggressive classifier70.0±4.6Multilayer perceptron47.0±6.1Extra tree classifier79.0±4.4Proposed model86.0±3.5As illustrated in Tables12–14, the proposed approach achieved higher accuracy than other recent works under all the three radiology datasets.


## 6.4. Discussion

COVID-19 has affected millions of people around the world. The early detection of COVID-19 could help in stopping the spread. One of the most effective detections is screening the infected patients. Deep learning plays an affective role in this detection, and it is more accurate. There are many studies implemented in to detect COVID-19 using deep learning. In this research, we have used a deep learning neural network (convolutional neural network) to detect COVID-19 from different radiologists. Our model results are comparable to the state of the art because we have built a robust model. What made our model a comparable result is that we build the model using different layers (27 layers), adjusting hyperparameters, using effective percentage in dropout, size of the filters in convolutional and pool layers, and using a suitable way in preprocessing. These layers include input, convolutional, max-pooling, dropout, flatten, dense, and output. The input layer accepts input grayscale images of size 128 × 128 and uses 64 filters of size 3 × 3. An ReLU activation function is used in the input layer and all hidden layers. Following the max-pooling layer is a dropout layer to avoid overfitting. This drops out different neurons in the hidden layer. The percentage of neurons to drop should be specified when using the dropout function. We drop 30%. Next are two convolutional layers, both with 128 filters of size 3 × 3, then a 2 × 2 max-pooling layer, and a dropout layer to drop 30% of neurons. We add three convolutional layers with 256 filters, each with size 3 × 3, and then a max-pooling layer with the same parameters as the previous pooling layer. We also drop 30% of the output neurons. We continue increasing filters in more layers, adding three convolutional layers with 512 filters in each layer, with the same filter size as previous layers. A max-pooling layer follows this stack of layers, and 30% of the neurons are dropped. Two stacks, the same as previous layers, are added using the same parameters. Moreover, the model got significant accuracy among the state of the art. It trained in different datasets with different hyperparameters using same structure of the model. That indicates that the structure of the model is significant. Also, the model was compared with different machine learning techniques to show the difference in accuracy between deep learning and regular machine learning algorithms.


## 7. Conclusions

We developed a model to efficiently detect COVID-19 from different radiology techniques and showed its robustness on X-ray, CT, and MRI datasets. We used a CNN to build the deep learning model, which gives adequate image classification. To show the performance of the proposed model, many experiments were performed against each dataset. In the first experiment, we built a model using a CNN with different layers and trained it on the first dataset, and the same model constructor was used to train the other datasets. For each dataset, we adjusted the hyperparameters for the model to get a robust model. In the second experiment, we utilized different machine learning algorithms on each dataset (in the absence of the proposed model). This demonstrated the importance and significance of the proposed model. Regardless of the lack of instances in a dataset, our model had high classification accuracy for COVID-19. Finally, the classification rates of our technique were compared to those of the previous work, and the developed approach presented the best performance on various radiology datasets.The proposed system was tested and validated in a controlled environment. In future research, we will deploy the system in real healthcare systems in which COVID-19 is easily detected from real images.



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