Covid-19 Detection Using Deep Neural Networks

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 06 | June 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 355
Covid-19 Detection Using Deep Neural Networks.
Prakash Upadhyay, Dhairya Shah, Jigar Vaishnav, Miloni Shah
Thakur College of Engineering and Technology (TCET)
Kandivali (East), Mumbai - 101
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Abstract - Covid-19 is inciting panic amongst people for
several reasons. It’s a new virus and currently there is no
vaccine or cure. Its novelty means that doctors and scientist
are not sure how it behaves and how it might evolve. The
World Health Organization (WHO) labeled the virus as a
pandemic. In this wave of panic a neural network that could
help in early detection of COVID 19 in patients would help put
people’s minds at ease. The proposed Neural Network will
input the symptoms that people are experiencing along with
other data relevant to prediction of COVID 19 like age, recent
travel history, etc. These features will be entered into the
Neural Network, and it will predict the probable chance that
you might have contracted COVID 19. This Neural Network
will not only impact people’s lives but also create a sense of
awareness among people.
Key Words: Artificial Intelligence, Reinforcement
Learning, Deep Neural Networks, Covid-19, CNN.
1. INTRODUCTION
The coronavirus outbreak has not only infected millions of
people but has also led to thousands of deaths. This virus,
despite having lower fatality rate, has caused thrice the
number of deaths as compared to the combined number of
deaths caused by both MERS and SARS. This is majorly
caused by the fact that COVID 19 is highly contagious. It has
been observed that the symptoms of COVID-19 are like that
of common influenza, which makes it difficult to detect. Due
to these factors, it is critical todetectpositivecasesofCOVID-
19 as early as possible so that we can prevent the spread of
this pandemic. It is extremely necessary to make diagnostic
tools that can aid in the detection of COVID-19. Deep neural
network is a technology where there are multiple layers
along with an input and output layer. Deep Neural Network
aims at learning featurehierarchies. Weareusingsymptoms
of COVID 19 patients as the input features of deep neural
networks. The symptoms will include running nose, fever,
cold, cough, sore throat and it will also consider the age,
history of pre-existing diseases and recent travel history of
the person. We will collect data from patients admitted
within various hospitals, this data will include records of
patients who tested positive as well as those who tested
negative for COVID-19. Our neural network model will
analyze the symptoms of thousands of patientsandalsotake
into account external factors like their recent travel history,
pre-existing diseases and all of this data will help it to
generatepreciseprediction.Thisdeepneuralnetworkmodel
will allow us to predict the chances of a person being COVID
19 positive atan early stage and it willalso createawareness
among other individuals. A huge amount of data can be
pooled from millions of people, and this will enable us to
improve the accuracy of the model.
2. DESIGN
Fig.: Sequence Diagram for Application Architecture
3, The Application Architecture
Application Architecture focuses on helping the user
understand how the application worksinthe real world.To
use the application, user access the web-based application
using internet browser, after the access is granted,theuser
is presented with a friendly userinterfacein whichuserhas
to provide the information askedintheapplicationwhichis
then sent to the trained neural network. It includes
information such as the symptoms of Covid – 19, age, and
previous medical history and diseasesetc.Thisinformation
would help the trained neural network to find the
probability that the user is suffering from Covid-19 and
suggest helpful tips to ensure safety and good health. Thiis
supposed to be the functioning of the application when the
user will enter the details on the web application. The
application can be used as many times as the user feel that

the symptoms are changing and as the trained neural
network is dynamic, with new patient's data, the network
gets better and the probability of the user suffering from
Covid-19 gets more accurate.
The deep neural network responsibleforpredictionconsist
of three parts
1) Input Layer
2) Hidden layers
3) Output Layer
Chart: Deep Neural Networks
To use the deep neural network, first we have to train the
network, Training of DNN requires data which can be
collected by various hospitals, the data which contains the
symptoms and information of the patient such as age and
previous diseases and whether that patient is sufferingfrom
Covid-19, after collecting the data, it has to be sent for pre-
processing and then fed into the input layer of the neural
network with input features such as the age, symptoms of
Covid-19, previous medical disease etc. The input layer
supplies these features to the first hidden layer and the
weights of these neurons compute the output value of that
layer. After computing the output values, they are mapped
by a nonlinear activation function. The result of the
activation function is then forwarded down to the next
hidden layer. This process continues up to the output layer
where a sigmoid function is used to compute the final
probability of the Covid-19 detection which lies between 0
and 1.
Fig.: Sigmoid Function
The sigmoid function is the activation function more
commonly known as squashing function, which limits the
output range between 0 and 1.
Fig.: Leaky Relu
The Leaky ReLU function modifies the function to allow
small negative values even when the output is zero such
that the negative values are not neglected while training
the neural network. After the training of the deep neural
network, It is integrated with the web application.
The user can then send his data through the web
application, which is then processed by the deep neural
network and the result is returned to the user interface
through the web application.
4. Experimental Results:
After executing the web application with the cumulated
dataset, we found the accuracy of the neural network to be
65 %, however the accuracyof the model canbeincreasedif
the model is trained with the live dataset from the hospitals
and if the hyper parameters are tuned effectively.

Here the user is entering the information and then the
information is sent to the trained neural network and the
sent back to user’s interface.
Fig.: Application Interface
Fig.: Application Interface Result
5. Addressing the Emerging Challenges
Not only increasing the overall accuracy but also decreasing
the number of False Negatives will be a major challenge
before using the proposed solution on real-world entities
because a minor flaw in the system can put the life of the
patient in danger. A strong model generalizes the examples
rather than memorizing them. Therefore, over-fittingshould
be minimized by using regularization techniques. Alsooneof
the major concern is to remove outliers from the training
data, so that these data points don’t influence the model in
the wrong direction.
6. Advantages
Predictive computing tools provide powerful insights that
can help us with early detection of COVID-19. These
technologies are still in their infancy stages but with
adequate amount of data collected from hospitals we can
generate tools that can produce precise results. With our
deep neural network model, individuals with COVID-19
symptoms or recent travel history can simply input the
features/symptoms that they are experiencing into the
model and expect to get a near accurate percentage of them
having contracted COVID-19. This knowledge can help
people self-quarantine themselves and prevent further
propagation of COVID-19. we can generate tools that can
produce precise results. With our deep neural network
model, individualswithCOVID-19symptomsorrecenttravel
history can simply input the features/symptoms that they
are experiencing into the model and expect to get a near
accurate percentage of them having contracted COVID-19. If
the percentage predicted by the model is high then that
knowledge can help people self-quarantine themselves and
prevent further propagation of COVID-19. If the predicted
percentage is low than this early prediction can also
suppress unwarranted panic amongst people. Sometimes
people having only a few symptoms of COVID-19 may avoid
getting medically tested but they can use our model to input
the features to get a predictive analysis of the chances of
them being infected. This can enablepeopletorealizeifthere
is a real need to get medically tested or not. An early
prediction model can also lower the mortality rate as the
people with high chances of being infectedbyCOVID-19may
seek early medication.
7. Further Work And Future Scope
Considering the power of deep neural networks, we are not
limited to predicting the probability of Corona detection.
Given enough data these deep neural nets can be trained to
predict the mortality rate or the risk of a patient's life. An
early detection of whether the infected patient’s condition
will further degrade can help the medical examiners to
arrange for necessary equipment to save the patient’s life.
This will be a great leap towardsadvancementinthemedical
sector. The collection of data for such features will surely
require great efforts and enhance the complexity of the
model along with its usefulness. Such a complex network of
neurons will require greater computation power.
Neural networks can be improved using techniques like
Hyperparametertuningand regularization.Therearecertain
parameters whose values are set beforethelearningprocess
begins. They are known as hyperparameters. If
these parameters are tuned effectively the performance of
the neural network can be increased by a massive amount.
Learning rate is one of the mostimportanthyperparameters.
This parameter corresponds to the speed at which the
gradient decent converges to find the minima of loss
function. Using an optimum value of learning rate will help
the neural network set the weights and reach the minima of
the loss function with great speed.
Sometimes the model learns even the tiniest details present
in the data. This can give rise to overfitting. Regularization is
an effective technique to prevent overfitting of the data. It

helps to reduce the complexity of the model and decreases
weights to avoid fitting noise in the data. Hence the reduced
complexity results in the neural network converging faster
in the forward direction and the learning begins to improve.
Using these techniques, the predictive ability of deep neural
networks can be increased to a great extent.
8.Conclusion
A detailed study of the rising amount of cases and the
factors affecting the patients of Covid-19, can help in
making the deep neural network more efficient and
accurate, thus allowing people to check the probability of
them having Covid-19 from home, which in turn will save
lives of thousands of medical healthcare workers who are
risking their life and exposing themselves during this
pandemic to test and treat patients.
The proposed approach is a preliminary attempt at
making the deep neural network for the early detectionof
Covid-19 and thus stopping its fast propagation before
everyone is affected by it.
9. REFERENCES
1.Ezz El-Din Hemdan, Marwa A. Shouman, Mohamed
Esmail Karar: COVIDX-Net: A Framework of Deep
Learning Classifiers to Diagnose COVID-19 in X-Ray
Images, arXiv:2003.11055 [eess.IV] (March 2020)
2.ROBERTHECHT-NIELSEN: III.3 - Theory of the
Backpropagation Neural Network* Computation,
Learning, and Architecture (1992), Pages 65-93
3.Linda Wang, Zhong Qiu Lin and Alexander Wong COVID-
Net: A Tailored Deep Convolutional Neural Network
Design for Detection of COVID-19CasesfromChestX-Ray
Images, arXiv:2003.09871 [eess.IV] 11 May 2020
4.Prabira Kumar Sethy , Santi Kumari Behera: Detectionof
coronavirus Disease (COVID-19) based on Deep
Features, doi: 10.20944/preprints202003.0300.v1 (19
Mar 2020)
5.Alfredo Canziani & Eugenio Culurciello,AdamPaszke:AN
ANALYSIS OF DEEP NEURAL NETWORK MODELS FOR
PRACTICAL APPLICATIONS, arXiv:1605.07678v4[cs.CV]
14 Apr 2017

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