A New Deep Convolutional Neural Network Learning Model for Covid-19 Diagnosis

Advances in Engineering: an International Journal (ADEIJ), Vol.3, No.3
1
A NEWDEEP CONVOLUTIONAL NEURAL NETWORK
LEARNING MODEL FOR COVID-19 DIAGNOSIS
Henry Li1
and Chen2
1
Princeton International School of Mathematics and Science 19 Lambert Dr, Princeton NJ
2
Chinese Academy of Science, Academy of Mathematics and System Science 55 ZhongGuanCun
East Road, HaiDian District, Beijing
ABSTRACT
Ever since 2019, people from all over the world are talking about infection with SARS-CoV-2, also known
as COVID-19. The symptoms range from asymptomatic conditions to fatal disease, with lung injury most
frequently being the result of it. As time flies during the pandemic, the role of medical imaging has been
more cortical than ever, with computed tomography being an alternative testing method combined with
polymerase chain reaction testing to have a broader role. However, only performing medical imaging
testing with suspected patients without classifying whether or not the patient has COVID-19 is not
practical. Nevertheless, in many different areas, excellent pulmonology doctors are in extreme shortage for
most of the developing counties. Even in developed countries, doctors are too busy with diagnosing and
curing patients, so the need for classification for the medical image of patients to see whether they have
COVID-19 or not is of high necessity. Moreover, studies of chest radiographs and CT images with
applications of artificial intelligence have shown of big importance and necessity. In this paper, we mainly
focus on the usage of deep learning and machine learning for categories with findings typical COVID-19
infected images and an application of medical images and testing of the system.
KEYWORDS
SARS-CoV-2, COVID-19, Lung injury, Medical imaging, pulmonology,Computed tomography, Polymerase
chain reaction, Imaging classification, Deep learning, Machine learning
1. INTRODUCTION
It is generally believed that an infectious disease called COVID-19 was discovered in Wuhan,
China in December 2019. The cause of it is generally believed to be Severe Acute Respiratory
Syndrome Coronavirus 2 (SARS-CoV-2). In 2020, especially from March to June, the number of
people infected with COVID-19 each day has tripled in many European and American Countries.
Coronavirus disease is highly contagious. With the increasing number of confirmed cases, how to
control this disease is becoming a public health challenge. The World Health Organization
(WHO) declared the epidemic as a public health emergency of international concern (PHEIC) on
January 30, 2020 [1], and declared it a pandemic on March 11, 2020 [2].
Researchers say that cases of infection can be confirmed by performing a test called reverse
transcription-polymerase chain reaction (RT-PCR) [3]. More than 23 million COVID-19 positive
cases have been reported until August 2020 [4]. However, the difficulty of quality control during
sample preparation has resulted in a high false-negative rate. Therefore, accurate and fast image
processing tools, especially those based on X-ray and computed tomography (CT), can help
doctors. Otherwise, even if there are no common COVID-19 symptoms, suspicious patients will
be hospitalized or isolated until laboratory test results are clear. The SARS-CoV-2 infection

2
could cause asymptomatic to severe and even fatal diseases. The most common mortality rate is
acute lung injury. During the pandemic, the role of imaging has become highly important.
Compared with RT-PCR testing, CT was initially an alternative method and a superior testing
method but later evolved into a more limited effect based on specific indications. In the early
stages of the pandemic, several chests imaging classification and reporting schemes were
developed for patients suspected of COVID-19 to help classify patients with limited RT-PCR
testing and unknown performance levels. Some studies have been carrying out observing lung
involvement on chest radiographs and CT images, and they indicate that it is related to critical
illness. In addition to pulmonary manifestations, cardiovascular complications such as
thromboembolism and myocarditis have also been attributed to COVID-19, which sometimes
leads to neurological and abdominal manifestations. [5] Finally, artificial intelligence has shown
promise that can be used to determine the radiology and CT diagnosis and prognosis of COVID-
19 pneumonia. [6] Therefore, the collection of chest images such as X-rays and CT scans plays
an important role in limiting the spread of the virus and fighting COVID-19 at the appropriate
stage of treatment. Artificial intelligence-based technology is used to make quick decisions in
saving lives. [7] Among all possible methods, artificial intelligence-based methods tend to help
doctors as an effective tool for diagnosing COVID-19. The image acquisition, segmentation,
classification, and subsequent diagnosis phases developed between 2019 and 2020 are widely
used [8].
2. DEEP LEARNING NETWORK AND MODELS
Deep Learning Networks are AI functions that imitate how the human brain works when
processing data and creating patterns for decision making. It is a branch of machine learning of
artificial intelligence. Its networks are capable of learning on their own without supervising. To
train the network, unstructured or unlabeled data are being inputted. Until then, Artificial
intelligence and machine learning technologies improved the accuracy of Covid-19 diagnosis.
Also, most of the widely used deep learning models and methods were implemented and a small
amount of data was used for COVID-19 diagnosis.[9] However, due to the rapid outbreak of
Covid-19, there are not many real data sets available to the community. It is necessary to
combine the observation and image information to make diagnoses of COVID-19. [10] AI can
introduce more alternative ways to the medical system, and learn from multi-modal data to
capture disease characteristics to obtain reliable results used for COVID-19 diagnosis for timely
treatment. [9]
2.1. Convolutional NeuralNetwork
One kind of artificial neural network that is most commonly applied to analyze images is
convolutional neural networks, which have properties of shift-invariant and space invariant based
on the shared-weight structure of the convolution filters that slide along input features and
provide translation equivariant responses known as feature maps. [11] Most of the convolutional
neural networks are equivariant, which are being applied in for example image and video
recognition, image classification, image segmentation, medical image analysis, natural language
processing, brain-computer interfaces, and financial time series [12].

3
Figure 1: An example of the process of a convolutional network
Convolutional neural networks were designed base on the biological processes between neurons
that resemble the human brain cortex. Individual cortical neurons respond to stimulations only in
the receptive field, which is a restricted region of the visual field, and the receptive fields of
different neurons partially overlap such that they cover the entire visual field. [13]
Convolutional neural networks have multilayer perceptron, which is thoroughly connected
networks and every neuron in each layer is connected to all other neurons in the next layer. [14]
The property of full connectivity of these networks makes them capable of processing overfitting
data. CNNs take a different approach towards regularization: they take advantage of the
hierarchical pattern in data and assemble patterns of increasing complexity using smaller and
simpler patterns embossed in their filters [15].
Convolutional neural networks have a relatively shorter pre-processing procedure compared to
other neural networks used for image classification algorithms. This means that the network
learns to optimize the filters through automated learning, whereas in traditional algorithms these
filters are hand-engineered, and the independence from prior knowledge and human intervention
in feature extraction is a major advantage. [16]
2.2. U-net
What it takes to train deep convolutional networks are thousands of annotated training samples.
An efficient sliding-window convolutional network and training strategy that relies on the strong
use of data augmentation to use the available annotated samples more efficiently is named U-net
[17]. This network structure consists of a contracting path for capturing contexts as well as the
asymmetric expanding path that enables localization precisely, and it is also capable of training
an end-to-end result from very few images and outputs the best and prior method and it is also
extremely fast. Segmentation of a 512x512 image takes less than a second. [17] In the last few
decades, deep convolutional networks have successfully finished a huge amount of visual
recognition tasks. One of the most typical applications of convolutional networks is imaging
classification, where the output for an image is a single class label. However, in many visual
tasks, especially in biomedical image processing, the desired output should include localization.
[17] Also, thousands of images used for training are hard to fulfill in the medical task. So, the
idea of training a network in a sliding-window set up to predict the class label of each pixel by
providing a local patch around that pixel as input has been performed by Ciresan et al, while this
network can localize while the training data (counted by patches) much larger than the number of
training images [18]. Due to the property of convolution, the output image is smaller than the
input images with a constant ratio of border width.

4
Figure 2: An example of a U-net structure for 32x32 pixels. Each blue box corresponds to a multi-channel
map. The number of channels is shown on top of the box while the x-y-size is shown at the lower left edge
of the box. White boxes represent copied feature maps, and the arrows denote the different operations. [17]
The network structure in Figure 2 consists of a contracting path, which is on the left side, and an
expansive path which is on the right side. The former follows the typical structure of a
convolutional network, which consists of the repeated application of two 3x3 unpadded
convolutions, each followed by a rectified linear unit (ReLU) and a 2x2 max pooling operation
with stride 2 for down-sampling. [19] By doubling the number of feature channels for each
down-sampling step in the expansive path, which consists of an up-sampling of the feature map
followed by a 2x2 convolution called “up-convolution” that halves the number of feature
channels, a concatenation with the correspondingly cropped feature map from the contracting
path with two 3x3 convolutions, each followed by a ReLU. [20] At the final layer, a 1x1
convolution is used to map each 64-component feature vector to the desired number of classes
which in total, the network has 23 convolutional layers. [17]
A high momentum of 0.99 isused, as the energy function is definedwith a pixel-wise and soft-
max with the cross-entropy loss function. The soft-max is defined as
𝑝𝑘(𝑥) =
exp⁡
(𝑎𝑘(𝑥))
∑ exp⁡
(𝑎𝑘′(𝑥)
𝐾
𝑘′=1
)
⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(1)
where 𝑎𝑘(𝑥)stands for the activation for channel k at the pixel position x ∈ Ω with Ω ⊂ℤ2
, and
K is the numberof classes and 𝑝𝑘(𝑥) is the approximated maximumfunction.
Thecross entropy then penalizes at each position the deviation of 𝑝𝑙(𝑥)(𝑥) from (1) using
𝐸 =⁡∑ 𝑤(𝑥)log⁡
(𝑝𝑙(𝑥)(𝑥))
𝑥⁡∈⁡𝛺 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(2)
where 𝑙 : Ω → {1, …, K} is the true label of each pixel and w: Ω → ℝ isa weight map that gives
some pixels more importance in thetraining. [17]
By pre-computing the weight map for basic segmentation to recompense the different pixels
frequencies from a certain class in the training data set, and also forcing the network to learn
small separation borders being introduced between each element, the separation border is
computed using morphological operations. [21] The weight map is then computed as

5
𝑤(𝑥) = 𝑤𝑐(𝑥) + 𝑤0 ⁡∙ exp (−
(𝑑1(𝑥)⁡+𝑑2(𝑥))
2
2𝜎2 )⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(3)
where 𝑤𝑐 : Ω → ℝ is the weight map for balancing the class frequencies, 𝑑1: Ω → ℝ means the
distance to the border of the nearest cell and 𝑑2: Ω → ℝ the distanceto the border of the second
nearest cell. In our experiments we set 𝑤0 = 10 and𝜎= 5 pixels [17].In a deep network, many
convolutional layers could be foundwith different paths throughoutthe whole network, which
makes a good initialization weight extremely important sinceparts of the network might give
excessive activations, and other parts never contribute otherwise. Ideally, the initial weights
should be adapted such that each feature map in the network has approximately unit variance.
[22] For a network with alternating convolutional and ReLU layers, this can be achieved by
drawing the initial weights from a Gaussian distribution. [23]
3. APPLICATION OF DEEP LEARNING MODELS TO RECOGNIZE COVID-19
INFECTED LUNG IMAGES
The main purpose of this research is to train the network tolet it recognize the COVID-19
Infected Lung Images. All the images are open-source chest CT images from Società Italiana di
Radiologia Medica e Interventistica (Italian Society of Medical and Interventional Radiology)
[24], National Institutes of Health (NIH), the Radiological Society of North America
(RSNA),[25] the American College of Radiology (ACR), Centers for Disease Control and
Prevention (CDC)[26].
3.1. TensorFlow Coding
TensorFlow is Google Brain's second-generation system, it is an open-source software library for
machine learning, often focus on the training and inference of deep neural networks. [27] U-net
models are available on TensorFlow, which is used to create a virtual environment for the
networking building of this learning program.
3.2. Applications and Final Results
The used images are from the website mentioned before to train the network. The images in these
websites are typical with labeled information, which is important for the training. We train the
traininggroup images by classifying them. In total, there are 1000 images for training the
network, and 100 for testing the network, and they are all being concluded into a dataset.
Figure 3: A part of the training image set

6
So first, inputting the training image data set allows the program to analyze and get a conclusion
of the data distribution. We want to put the images into 4 groups, which are train images, train
labels, test images, and test labels. All of the pictures are whether healthy lung chest CT images
or COVID-19 infected lung chest CT images. So, these two labels are the ones that the program
will be classicizing on.
Before we train the network, we want to see the results of input.
However, the input images are not the same size, so we want to pre-process the images for
different images, there is the need to modify them into the same size.
In order to test the size of the images, we show the first 25 images in the dataset.
After all the preparation is done, we will begin to build our deep learning network. The first step
is to set up the layers.The basic building blocks of a neural network is layers. Layers can extract
representations from the data given.

7
To begin with, the layer: tf.keras.layers.Dense hasa parameter that is learned in the process of
training.The first layer: tf.keras.layers.Flatten of this networktransforms the format of the images
from two-dimensional arraysto one-dimensional arrays. Then, after the pixels are flattened, the
network consists of a sequence of two tf.keras.layers.Dense layers will be fully connected neural
layers. The first Dense layer has 128 nodes. The second and last layer returns a logits array with a
length of 10. Each node contains a score that indicates the current image belongs to one of the 10
classes.[28] In this case, another network is also available.
Training the neural network model requires the following steps: 1) Input the training data into the
model, which are train_images and train_labels arrays in this example. 2) The model learns to
correlate images and labels. We ask the model to make predictions about a test set—in this
example, the test_images array. [29] 3) labels from the test_labels array match the predictions
[30]. To start training, we need to call the model.fit method. As the model trains, the loss and
accuracy metrics are displayed. This model reaches an accuracy of about 0.89 on the training
data. [17]
The accuracy of the testing dataset is not as good as the accuracy of the training dataset, this gap
between the training accuracy and the test accuracy is called overfitting. [31] It happens when a
machine learning model does not perform as well as on new inputs other than the training data.
An overfitted model "memorizes" the noise and details in the training dataset to a point where it
negatively impacts the performance of the model on the new data [32]. There are a few ways to
prevent overfitting, including 1) Simplifying the model, 2) Early stopping, 3) Use data
augmentation, 4) Use regularization and 5) Use dropouts. [33]
After the model is trained, we can then make predictions about our testinggroup of images.
Adding a soft-max layer to convert logits to probabilities will be easier to interpret.After this step,
we will make a plot of how the classification class predicts the result.

8
We can use this model that we trained to first make predictions about some images for the first
image in the testing groups, also for prediction array.
Last but not least, our last goal is to make predictions about a single image based on the trained
model.The tf.keras models are optimized to make predictions on a series of examples at once.[17]
The method: tf.keras.Model.predict returns a list of lists for each image in the batch of data. In
the end, it predicts the correct label as expected.
4. CONCLUSION
After comparing the characters of the network and the already-known characters of chest CT
images with lung affected with COVID-19 [34], the COVID-19 infected lung images characters
includes the presence of ground-glass opacities, presence of consolidation, presence of nodules,
presence of a pleural effusion, presence of thoracic lymphadenopathy, airways abnormalities that
include airway wall thickening, bronchiectasis, and endoluminal secretions, and last but not least,
the presence of underlying lung disease such as emphysema or fibrosis. [35]

9
Figure4: COVID-19 infected chest CT lung Images provided by S.S. Oncological Radiology San
Giuseppe Moscati Hospital, Taranto. A patient of a 51-year-old man presented himself to the hospital
emergency department with an 8-day fever, chest tightness, and mild dyspnea. The image suggests that this
patient with COVID-19 pneumonia manifests at thoracic CT investigation in the form of bilateral
subpleural GGO opacities with aerial bronchogram, ill-defined margins, and a slight predominance in the
right lower lobe [36].
5. DISCUSSION
The ground-glass opacification is defined as an area of increased lung attenuation with
preservation of bronchial and vascular margins [37], in other words, an area of increased hazy
lung opacity with vessels and bronchial structures may still be seen. While pulmonary
consolidation was defined as opacification with obscuration of margins of vessels and airway
walls [38], in other words, a region of normally compressible lung tissue that has filled with
liquid instead of air.
An important direction for future research is to alter the model used in the research to see if the
images could also be classified into the correct group. Or using more images to test the stability
of the network, maybe actually using it in medical applications. Also, noticing that we have
concluded some important characteristics of the COVID-19 infected lung images, we could
create more labels that include different kinds of symptoms. For example, labels with ground-
glass opacification, or labels of thoracic lymphadenopathy. However, it requires the algorithm
and the network to be more complicated, with more layers of convolutional network or more
complicated models. Once finished, it would become a more mature neural network.
6. ACKNOWLEDGMENTS
I would like to express my gratefulness to my mentor Dr. Chong Chen of Chinese Academy of
Science. He has provided guidance and advices. I would also like thank Ms. Betty Wang for
providing me this research opportunity. Last but not least, I would like to thank the organizers of
the Yau Awards for giving me the platform to show my work.
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