OPEN ACCESS
COVID19 appeared in December 19, 2019 in Wuhan, China. This disease has spread to almost all countries in a short time. Countries take a series of stringent measures, including the prohibition of going out to prevent the virus that spreads COVID19 disease. In this paper, we aimed to diagnose COVID19 disease from X_RAY images by using deep learning architectures. In addition, 96.30% accuracy rate has been achieved with the hybrid architecture we have improved. While developing the hybrid model, the last 5 layers of Resnet 50 architecture were ejected. 10 layers were added in place of the 5 layers that were removed. The count of layers, which is 177 in the Resnet50 architecture, has been increased to 182 in the hybrid model. Thanks to these layer changes made in Resnet50, the accuracy rate has been increased more. Classification was performed with AlexNet, Resnet50, GoogLeNet, VGG16 and developed hybrid architectures using COVID19 Chest XRay dataset and Chest XRay images (Pneumonia) datasets. As a result, when other scientific works in the literature are examined, it is finalized that the improved hybrid method offers better results than other deep learning architectures and can be used in computeraided systems to diagnose COVID19 disease.
Covid19, deep learning, image processing, Resnet50, hybrid model
The new corona virus (COVID19) disease that has emerged in Wuhan, which is located in Hubei province of China, has no known vaccine and no effective treatment method [1]. Rapid kits and PCR kits are available for diagnosis of the disease. Fast kits have lower accuracy than PCR kits. Experts are trying to develop vaccines for the treatment of the disease [2]. It is said that even if the vaccine is developed according to the best situation, all tests and market launch may take up to a year [3]. The COVID19 virus has spread to many countries as of now. The number of cases and the number of people who lost their lives is quite high [4]. Early diagnosis of COVID19 disease and quarantine of the infected patient are vital for the massive spread of the disease and to combat the disease [5].
In this paper, Resnet50 architecture was used as a basis. A new hybrid model was proposed by improving the Resnet50 architecture. Thanks to the developed Hybrid model, an accuracy of 96.30% was achieved. this accuracy rate is one of the highest accuracy rates in the literature.
The clinical symptoms of COVID19 disease in the patient are sudden fever, cough, shortness of breath and respiratory distress [6]. However, these symptoms are not specific. It was determined that pneumonia was detected in chest CT scan in asymptomatically infected patients and that the virus was positive as a result of pathogenic testing. Radiological imaging is a very important diagnostic tool for the diagnosis of COVID19 due to such situations [7].
More than 100 scientific articles have been published in the literature about COVID19 disease in a very short time. Among these studies, the classification of the chest images by computer and the ones related to machine learning methods are as follows;
In their study, Wang et al. Applied 217 images of 453 CT images of patients confirmed to be COVID19 in the CNN algorithm for the training of the system. They achieved a success rate of 83% accuracy [8].
In their study, Xu et al. Segmented candidate infection sites from the CT image set using a 3D deep learning model. They categorized these segments into segments using COVID19, InfluenzaA and unrelated groups of infections using the positionattention classification model with their corresponding confidence values. The data set they use contains 618 CT images. These images were performed by taking 219 CT samples from 110 COVID19 patients, 224 influenza A samples and 175 CT samples from healthy people. In their method, they classified COVID19 disease with an accuracy of 86.7% [9].
Wang and his colleagues proposed a new and effective Respiratory Simulation Model (RSM) in their work. With this model, 6 important clinical respiratory samples were classified. They have reached 94.5% accuracy rate with the system they have proposed [10].
In their study, Rao et al. Suggested that the possible case definitions of COVID19 can be determined more quickly with a mobile phonebased web survey using machine learning algorithms. They also stated that this would reduce the speed of propagation, which is sensitive [11].
Shan and colleagues used DLbased segmentation in their studies and used the "VBNet" neural network to segment COVID19 infected areas in CT scans. They used 219 COVID19 data. They stated that it showed high accuracy for quantitative evaluation, automatic infection site identification and POI measurements [12].
In their study, Gozes and colleagues have tried to develop artificial intelligencebased automated computerized imaging (CT) image analysis tools to detect, quantify and monitor COVID19 and to show that they can distinguish COVID19 patients from those without the disease. 157 patients in China and America were used in test studies. They stated that, using standard machine learning techniques and innovative artificial intelligence applications, together with a builtin computed tomography (CT) detection platform, it can be used as an effective tool for screening and early detection of patients who may have caught the COVID19 pathogen [13].
In the continuation of the paper, material and methods, Application and Results, Conclusion sections are examined.
In this article, it is aimed to classify diseases with deep learning architectures using COVID19 Chest XRay dataset and Chest XRay images (Pneumonia) datasets. Deep learning architectures, which are a subbranch of machine learning, have become very popular recently and these architectures are widely used. In this study, CNN architectures, a subbranch of deep learning, were used. The developed model has been compared with CNN architectures. The data sets used, the structure of the developed method and the layers used are examined in the following section.
2.1 Dataset
The COVID19 Chest XRay dataset and Chest XRay images (Pneumonia) datasets used in this study were taken from the Kaggle website, which is open access. The COVID19 Chest XRay dataset used consists of 136 data in total. 245 normal images, 162 pneumonia data of Chest XRay images (Pneumonia) data set were used [14]. The image samples used in the datasets are given in Figure 1.
Dataset 
COVID19 
Pneumonia 
Normal 
Number of Data 
136 
162 
245 
2.2 Structure of systems
In the hybrid method, Resnet50 architecture was used as the basement. The input layer of the Resnet50 architecture has been updated to 224 * 224 * 1. Later, the Convolution layer after the input layer was replaced. Finally, the five layers of the Resnet50 architecture were extracted and ten new layers were added instead.
Thanks to the new layers added, the accuracy rate of the resnet50 model has been increased. Resnet50 architecture has been used as the basis in the developed hybrid model, as it achieves high performance in biomedical images.
The reason for choosing the Resnet50 architecture is that instead of training a network from scratch, a trained network will be more efficient. The existing knowledge of this model has been used. After the changes made in the Resnet50 architecture, the number of layers increased from 177 to 182 [15]. The improved model is presented in Figure 2.
Figure 2. Architecture of the hybrid model
Added layers, parameter numbers and other features are presented in Table 1.
Table 1. Properties of layers used in hybrid model

Name 
Type 
Activations 
1 
Imageinput 
Image Input 
224x224x1 
2 
conv_1 
Convolution 
112x112x64 
172 
add_16 
Addition 
7x7x2048 
173 
relu 
Relu 
7x7x2048 
174 
conv_2 
Convolution 
7x7x32 
175 
batchnorm 
Batch Normalization 
7x7x32 
176 
dropout 
Dropout 
7x7x32 
177 
fc_1 
Fully Connected 
1x1x2 
178 
activation 
Relu 
1x1x2 
179 
maxpool 
Max Pooling 
1x1x2 
180 
fc_2 
Fully Connected 
1x1x2 
181 
fc1000_soft 
Softmax 
1x1x2 
182 
classoutput 
Classification Output 
 
This layer is the first layer of the developed hybrid model and other models. The images are first read from the input layer [16]. The input sizes of the hybrid model and other models used in the application are in Table 2.
Table 2. Input size of images
Model 
Input Size of Image 
Hybrid Model 
224 224 3 
GoogLeNet 
224 224 3 
AlexNet 
227 227 3 
Densenet201 
224 224 3 
InceptionV3 
299 299 3 
Resnet50 
224 224 3 
In this layer, the input image is reduced to a smaller size than the size of the filter used. NxN size filters can be preferred in this layer. The aim of this layer can be expressed shortly as producing feature maps [17]. The discrete time convolution process is presented in Eq. (1).
$s(t)=(x * w)(t)=\sum_{a=\infty}^{\infty} x(a) w(ta)$ (1)
w: kernel (filter), $x:$ input, $t:$ times, $s:$ Result $m, n: 0$
If twodimensional data is taken as input value, Eq. (2) is preferred.
$S(i, j)=(I * K)(i, j)$$=\sum_{m} \sum_{n} I(i, j) K(im, jn)$ (2)
The terms i and j indicate the locations of the new matrix acquired after the convolution process. The preferred method in this process is positioned so that the center of the filter is at the starting point.
If cross entropy is to be performed, Eq. (3) is used.
$S(i, j)=(I * K)(i, j)$$=\sum_{m} \sum_{n}(i+m, j+n) K(m, n)$ (3)
2.2.3 Activation function
Activation functions are often preferred in artificial neural networks for nonlinear transformation processes. There are many activation functions developed in the literature. Relu, Sigmoid and Tanh are the most preferred among these activation functions. Relu was preferred in the hybrid model we developed [18]. Activation functions are frequently used in deep learning models. Relu, Sigmoid and Tanh activation functions are given in Eqns. (4), (5), (6).
Relu: $f(x)=\left\{\begin{array}{l}0, x<0 \\ x, x \geq 0\end{array}, f(x)^{\prime}=\left\{\begin{array}{l}0, x<0 \\ 1, x \geq 0\end{array}\right.\right.$ (4)
Sigmoid: $f(x)=\frac{1}{1+e^{x}}, f^{\prime}(x)=f(x)(1f(x))$ (5)
Tanh: $f(x)=\tanh (x)=\frac{2}{1+e^{2 x}}1, f^{\prime}(x)=1f(x)^{2}$ (6)
2.2.4 Normalization
This layer is preferred to normalize the output value produced by the convolution and fully connected layers. This layer briefly normalizes the layer output [19]. In this way, the training period of the network is shortened and the network performs the learning process more quickly. Eq. (7) is used to perform normalization.
$Y_{i}=\frac{X i\mu_{\beta}}{\sqrt{\sigma_{\beta}^{2}+\epsilon}}$ (7)
$\sigma_{\beta}=\frac{1}{M} \sum_{i=1}^{M}\left(X_{i}\mu_{\beta}\right)^{2}$ (8)
$\mu_{\beta}=\frac{1}{M} \sum_{i=1}^{M} X_{i}$ (9)
M: Number of input data
$X_i$: i=1 ... M
$\mu_\beta$: Average value of the stack
$\sigma_\beta$: Standard deviation of the stack
$Y_{i}$: New values resulting from normalization process
2.2.5 Dropout
The Dropout layer is used to prevent the network from memorizing. Model can memorize training data and perform overlearning. If the network goes into an extreme learning process, it loses its ability to learn. With the dropout process, some nodes in the network are randomly disabled [20]. In this way, the network is prevented from memorizing. Dropout process cannot be used in test and confirmation steps. A general dropout process is shown in Figure 3.
Figure 3. Dropout process
2.2.6 Fully connected
The Fully Linked layer reduces the input data to a onedimensional matrix format. The number of fully bound layers used in each architecture is different [21]. Eq. (10) is used for this process.
$u_{i}^{l}=\sum_{j} w_{j i}^{l1} y_{j}^{l1}$ (10)
$y_{i}^{l}=f\left(u_{i}^{l}\right)+b^{(l)}$ (11)
l: Layer number,
i, j: Neuron number,
y^{li}: the value in the output layer created,
w^{l1}_{ji}: The weight value in the hidden layer,
y^{l1}_{i}: The value of input neurous
u^{l}_{i}: The value of the output layer
b^{(l)}: deviation value.
In this study, the number of classes (COVID19, Pneumonia and Normal) is 3. For this reason, the output value of the fully connected layer 3 of our hybrid model is entered.
2.2.7 Pooling layer
This layer is a preferred layer after the convolution layer. With the pooling process, the information from the convolution layer is simplified. The most common pooling methods are average pooling and maximum pooling. In pooling, the network does not perform any learning. NxN sized filters are preferred for pooling process [22]. The pooling process is given in Eq. (12). Maximum pooling is used in the developed hybrid model.
$S=w 2 * h 2 * d 2$ (12)
$w 2=\frac{(w 1f)}{A+1}$ (13)
$h 2=\frac{h 1f}{A+1}$ (14)
$d 2=d 1$ (15)
A = number of steps used
w1 = width of the input image,
h1 = height of the input image,
d1 = depth value of input image size,
f = filter size
S = Size of manufactured image.
Maxpooling was used in the hybrid architecture we developed.
2.2.8 SoftMax
It is accessed prior to the SoftMax classification layer [23]. performs the probabilistic computation created on the network and generates a value for each class. The SoftMax process is given in Eq. (16).
$P(y=j \mid x ; W, b)=\frac{\exp ^{X^{T} W_{j}}}{\sum_{j=1}^{n} \exp ^{X^{T} W_{j}}}$ (16)
W, b, s a: weight vector.
2.2.9 Classification
This layer is the last layer of the architectures used to produce output value [24].
In this paper, COVID19 xray chest images, and Chest XRay images (Pneumonia) images obtained before COVID19 disease appeared, were combined. It is aimed to classify these combined data sets with deep learning architectures and the developed hybrid model. While 80% of datasets are used for education, 20% are used for testing. The application was obtained in a computer with an i5 processor, 8 GB RAM memory in Matlab environment [25].
One of the most important criteria in CNN architectures is the confusion matrix [26]. Values such as Accuracy, Sensitivity, Specifity, F1 Score are calculated using the confusion matrix [27]. In summary, the confusion matrix can be said to be the photo of the trained network. In general, a confusion matrix structure is presented in Table 3.
Table 3. Confusion matrix

A 
B 
A 
TP 
FP 
B 
FN 
TN 
FP(FalsePositive): Data A was estimated as B and was placed in the wrong class.
FN(FalseNegative): The data B is estimated to be A but data is B.
TN(TrueNegative): The data B is estimated to be B and data is actually B.
Accuracy: It is the proportion of the number of accurately estimated data to the total size of data used [28]. The equation that calculates the accuracy value is given in Eq. (17).
Accuracy $=\frac{T P+T N}{T P+T N+F P+F N}$ (17)
The equation of the Sensitivity value obtained using the Confusion matrix is given in Eq. (18).
Sensitivity $(T P R)=\frac{T P}{T P+F N}$ (18)
The equation of the specificity value is presented in Eq. (19).
Specificity $(T N R)=\frac{T N}{T N+F P}$ (19)
Calculating F1 Score value is presented in Eq. (20), precision calculation in Eq. (21), Calculation of Recall value in Eq. (22), FPR in Eq. 23, FDR in Eq. (24) and FNR in Eq. (25).
$F$measure$=\frac{2 * \text {Precision} * \text {Recall}}{\text {Precision}+\text {Recall}}$ (20)
Precision $=\frac{T P}{T P+F P}$ (21)
Recall $=\frac{T P}{T P+F N}$ (22)
$\text {False Positive Rate }(F P R)=\frac{F P}{F P+T N}$ (23)
$\text {False Discovery Rate }(F D R)=\frac{F P}{F P+T P}$ (24)
$\text {False Negative Rate }(F D R)=\frac{F N}{F N+T P}$ (25)
Cnn architectures and training data used in the developed model are given in Table 4.
Table 4. Educational values of models
Solver Name 
Sgdm 
MaxEpochs 
4 
MiniBatchSize 
10 
Shuffle 
everyepoch 
ValidationFrequency 
6 
InitialLearnRate 
1.000e04 
Total Iteration 
172 
Table 5. Confusion matrix in the application

COVID19 
Pneumonia 
Normal 
COVID19 
True 
False 
False 
Pneumonia 
False 
True 
False 
Normal 
False 
False 
True 
Figure 4. Accuracy and loss curves of the improved model
Figure 5. Accuracy and loss curves of the Resnet50
After the network is trained, the performance values of the network are given in Table 6.
Accuracy, Sensitivity, Specificity, F1 Measure, FPR, FDR, FNR values were obtained by multiplying 100 in all architectures. These values were calculated separately for COVID19, Pneumonia and Normal images class.
The accuracy and loss curves obtained with the Resnet50 model are shown in Figure 5.
After the network is trained, the performance values of the network are given in Table 7.
The accuracy and loss curves obtained with the AlexNet model are shown in Figure 6.
After the network is trained, the performance values of the network are given in Table 8.
Table 6. Performance value of the Improved model
Confusion Matris 
26 
0 
1 
0 
49 
0 

1 
2 
29 


COVID19 
Pneumonia 
Normal 
Accuracy 
98.11 
98.11 
96.30 
Sensitivity 
96.30 
96.08 
96.67 
Specificity 
98.73 
1 
96.5 
F1 Score 
96.30 
98.00 
93.55 
FPR 
1.27 
0 
3.85 
FDR 
3.70 
0 
9.38 
FNR 
3.70 
3.92 
3.33 
Table 7. Performance value of the Resnet50
Confusion Matris 
26 
0 
1 
0 
48 
1 

2 
4 
26 


COVID19 
Pneumonia 
Normal 
Accuracy 
97.09 
95.24 
92.59 
Sensitivity 
92.86 
92.31 
92.86 
Specificity 
98.67 
98.11 
92.50 
F1 Score 
94.55 
95.05 
86.67 
FPR 
1.33 
1.89 
7.50 
FDR 
3.70 
2.04 
18.75 
FNR 
7.14 
7.69 
7.14 
Figure 6. Accuracy and loss curves of the AlexNet
Table 8. Performance value of the AlexNet
Confusion Matris 
27 
0 
0 
0 
48 
1 

5 
6 
21 


COVID19 
Pneumonia 
Normal 
Accuracy 
95.05 
93.20 
88.89 
Sensitivity 
84.38 
88.89 
95.45 
Specificity 
1 
97.96 
87.21 
F1 Score 
91.53 
93.20 
77.78 
FPR 
0 
02.04 
12.79 
FDR 
0 
02.04 
34.38 
FNR 
15.63 
1.11 
4.55 
Figure 7. Accuracy and loss curves of the GoogLeNet
After the network is trained, the performance values of the network are given in Table 9.
Table 9. Performance value of the Google net
Confusion Matris 
27 
0 
0 
0 
49 
0 

2 
8 
22 


COVID19 
Pneumonia 
Normal 
Accuracy 
98.00 
92.45 
90.74 
Sensitivity 
93.10 
85.96 
1 
Specificity 
1 
1 
88.37 
F1 Score 
96.43 
92.45 
81.48 
FPR 
0 
0 
11.63 
FDR 
0 
0 
31.25 
FNR 
6.90 
14.04 
0 
Figure 8. Accuracy and loss curves of the Vgg16
After the network is trained, the performance values of the network are given in Table 10.
Table 10. Performance value of the Vgg16
Confusion Matris 
25 
0 
2 
1 
43 
5 

1 
0 
31 


COVID19 
Pneumonia 
Normal 
Accuracy 
96.12 
94.29 
92.52 
Sensitivity 
92.59 
1 
96.88 
Specificity 
97.37 
90.32 
90.67 
F1 Score 
92.59 
93.48 
88.57 
FPR 
2.63 
9.68 
9.33 
FDR 
7.41 
12.24 
18.42 
FNR 
7.41 
0 
3.13 
Figure 9. Accuracy curves of models
Figure 10. Loss curves of models
Although the accuracy curves of the models used in this paper are shown in Figure 9, the loss curves are presented in Figure 10.
Accuracy value are given in Table 11 of all models used in the study.
The literature studies on COVID19 are presented in Table 12.
Table 11. Accuracy value of all models

Accuracy 
Hybrid Model 
96.30 
Resnet50 
92.59 
Vgg16 
91.66 
GoogLeNet 
90.74 
AlexNet 
88.89 
Authors/Year 
Methods 
Accuracy 
Wang et al. [8] /2020 
CNN 
83.00% 
Xu et al. [9] /2020 
Deep Learning 
86.7% 
Wang et al. [10]/2020 
Machine Learning 
 
Rao et al. [11] 
Machine Learning 
 
Shan et al. [12] /2020 
Segmentation 
 
Gozes et al. [13] /2020 
artificial intelligence 
 
COVID19 disease occurs in almost all countries of the world shortly after it appeared in China in December 2019. Countries take various measures to combat this disease, which has a high risk of transmission. The scientific world is spending an extensive time working on both the detection and treatment of the disease. In our study for the diagnosis of the disease, we tried to diagnose the disease using Xray images. In this study, CNN architectures were used to diagnose COVID19 disease. In this study, a hybrid model that we developed for the diagnosis of COVID19 was used. In this developed model, Resnet50, one of the CNN architectures, was used as the base. By removing 5 layers of the Resnet50 model, 10 new layers were added to the Resnet50. With this developed hybrid model, an accuracy rate of 96.30% was achieved. At the same time, results were acquired with AlexNet, Resnet50, Vgg16 and GoogLeNet architectures. The highest accuracy rate was achieved with the hybrid model we improved.
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