Covid-19 Detection Using CNN MODEL

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 186
Covid-19 Detection Using CNN MODEL
Sandhya Singh1, Rohit Shegokar2, Dakshit Shetty3, Prapti Shetty4
UG Student, Dept. of Electronics And Telecommunication Engineering, Rajiv Gandhi Institute Of Technology,
Mumbai-53, Maharashtra, India
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Abstract - The spreading increase in covid-19 cases is
inviting healthcare systems all over the world. With limited
testing accouterments, every case of respiratory illness
cannot treat using conventional ways. Deep Literacy has
boosted multi-fold in recent times, and it has played a
significant part in image classification, including medical
imaging. Convolutional Neural Networks (CNNs) have
performed well in detecting numerous conditions, including
coronary artery disease, malaria, Alzheimer's complaint,
and different dental conditions. The test also has a long
turn- around- time and limited perceptivity. The study
reveals that infected cases exhibit distinct radiographic
visual characteristics, fever, dry cough, fatigue and dyspnea.
Diagnosing possible covid-19 infections on casket X-ray may
help high-threat counter blockade cases while test results
are staying. X-ray machines are readily available at all the
healthcare centers, with no transportation time involved for
samples. This design proposes using a casket x-ray to
classify the case's selection for further testing and
treatment. The discovery is critical acute respiratory
pattern coronavirus responsible for coronavirus complaint
2019 (COVID-19), using chest X-ray images has life-saving
significance for both cases and doctors. Also, in countries
that can not buy laboratory accouterments for testing, this
becomes indeed more vital. This work shows how a change
in convolutional layers and an increase in dataset affect
classifying performances.
Key-Words COVID-19, :- Coronavirus, Epidemic, X-Ray,
Neural Network, Convolutional Neural Network, Data
Science, Artificial Intelligence
1. INTRODUCTION-
The ongoing epidemic of Coronavirus or COVID-19
complaint 2019-2020 has led to a global health care
extremity. The main challenge in this epidemic situation
on how to identify COVID-19 cases. Coronavirus or COVID-
19 is an infection complaint trigged by garçon acute
respiratory pattern COVID-19 (SARS-COV2). The
coronavirus complaint was originally linked in December
2019 in Wuhan, China, and has spread encyclopedically
worldwide. The case with Pneumonia of the Although
radiological imaging isn't recommended for diagnostics as
the case arrives in the clinic. The casket X-Ray image is
useful to observe treatment issues and comorbidities in
seriously ill cases. The discovery of Coronavirus from
casket X-Ray and its isolation from lung complaints with
indistinguishable darkness is a puzzling function that
relies on the availability of expert radiologists
Fig – 1: Novel Coronavirus Structure [1]
The paper explains an effective way to detect COVID-19
patients. It shows various methodology to get accurate
results. The major algorithms used are :
1. CNN model
2. Max Pooling
3. Data Augmentation
Detailed information about this is mentioned in the paper.
1.1 Problem Statement-
The proposed work then provides an intelligent machine
learning armature to descry COVID-19 disease using chest
X-ray images. The system proposes a new emulsion of
features uprooted by histogram-acquainted grade (HOG)
and CNN and bracket by CNN. Likewise, a modified
anisotropic prolixity filtering (MADF) fashion was applied
to exclude multiplicative patch noise from the test images.
The watershed segmentation fashion was used to identify
the fractured lung regions, which could further give
substantiation for the COVID-19 attacked lungs. The
proposed system majorly fastens on the delicacy to
produce a model which is trained by giving a dataset of

normal and COVID-19 positive patient’s X-Rays and to
develop its capability to successfully diagnose the case
through their X-Ray.
1.2 Objectives-
Develop a CNN model to detect COVID-19 positive patients
through their X-Ray. That is, to create a model which is
trained by giving a dataset of normal and COVID-19
positive patient’s X-Rays, and to develop its ability to
successfully diagnose the patient through their X-Ray.
1.3 LITERATURE SURVEY-
Ahammed proposed a deep neural network system where
CNN provided high accuracy (94.03%). The authors
trained the system with normal, pneumonia and COVID-19
patients’ chest X-ray images. The limitation of the work
was that a dataset with only 285 images was used for
developing the system, and this small number of data was
not perfect for training a deep learning-based system for
the COVID-19 prediction.
Chowdhury worked with chest X-ray images to develop a
novel framework named PD COVID Net based on parallel-
dilated CNN. In the proposed method, the authors used a
dilated convolution in the parallel stack that could capture
and stretch necessary features for obtaining a detection
accuracy of 96.58%.
Abbas proposed and validated a deep convolutional neural
network called decompose, transfer, and compose
(DeTraC) to detect COVID-19 patients from their chest X-
ray images. They proposed a decomposition mechanism to
check irregularities from the dataset by investigating class
boundaries for obtaining a high accuracy (93.1%) and
sensitivity (100%).
Amin used a deep learning method based on the ResNet-
101 CNN model. In their proposed method, thousands of
images were used in the pre-trained phase to recognize
meaningful objects and retrained to detect abnormality in
the chest X-ray images. The accuracy of this method was
only 71.9%.
El-Rashidy introduced a framework consisting of three
layers: patient layer, cloud layer, and hospital layer. A set
of data was collected from the patient layer using some
wearable sensors and a mobile app. A neural network-
based deep learning model was used to detect COVID-19
using the patient’s X-ray images. The proposed model
achieved 97.9% accuracy and 98.85% specificity.
● Khan developed a new architecture for the diagnosis of X-
ray images as the COVID-19 or normal using pre-trained
deep learning models like ResNet50, VGG16, VGG19, and
DensNet121, where VGG16 and VGG19 showed the best
accuracies. The proposed model consisted of two phases
preprocessing and data augmentation, and transfer
learning, and finally showed 99.3% accuracy.
3. Working Principles and Methodology to be
Adopted-
The suggested system considered the input of the X-ray
images to distinguish COVID-19.
First of all, this system converted images from RGB to
grayscale and linked the region of interest (ROI) by
removing the unwanted regions. Likewise, the system
considered two-point extractors histogram-acquainted
gradient (HOG) and CNN. First, the HOG methodology was
used to prize a point vector from the X-ray COVID-19
dataset. Also, the CNN system was used to prize another
point vector from the same images. These two features
were fused and used as the input to train the bracket
model. The number of features uprooted by one fashion
wasn't large enough to directly identify COVID-19. Still, the
emulsion approach of rooting features in two different
ways could give a large number of features for accurate
identification. Fusion was considered as a consecution
between the two individual vectors in this context.
Sprinkle-affected and low-quality X-ray images along with
good quality images were used in our trial for conducting
tests. However, the product preciseness maybe plants
advanced, if training and testing are performed with only
named good quality X- shaft images in an ideal situation.
Still, this doesn't represent a real-life script, where the
image database would be a blend of both good-and poor-
quality images. Thus, this approach of using different
quality images would test how well the system can reply
to similar real-life situations. A modified anisotropic
prolixity filtering method was employed to remove
multiplicative patch noise from the test images. The
operation of these ways could effectively overcome the
limitations in input image quality. Next, the point birth
was carried out on the test images. Eventually, the CNN
classifier performed a bracket of X-ray images to identify
whether it was COVID-19 or not.
List objectives using bullet points
● To gain optimal efficiency.
● Take large data sets.
● To reduce the time complexity.
● To increase accuracy.

Working Block Diagram
4. Algorithm Used-
⮚ CNN Model: -
In deep learning, a convolutional neural network (CNN/
ConvNet) is a class of deep neural networks, utmost
generally applied to dissect visual imagery. Now when we
suppose a neural network, we suppose matrix
proliferations but that isn't the case with ConvNet. It uses
a special fashion called Convolution. Now in mathematics
complication is a fine operation on two functions that
produces a third function that expresses how the shape of
one is modified by the other. But we don’t need to go
behind the mathematics part to understand what a CNN is
or how it works. Bottom line is that the part of the
ConvNet is to reduce the images into a form that's easier
to reuse, without losing features that are critical for
getting a good vatic nation.
• Working of CNN Model-
First of all, this system converted images from RGB to
grayscale and linked the region of interest (ROI) by
removing the unwanted regions. Likewise, the system
considered two-point extractors histogram-acquainted
gradient (HOG) and CNN. First, the HOG fashion was used
to prize a point vector from the X-ray COVID-19 dataset.
Also, the CNN system was used to prize another point
vector from the same images. These two features were
fused and used as the input to train the category model.
The number of features uprooted by one method wasn't
large enough to directly identify COVID-19. Still, the
emulsion approach of rooting features by two different
ways could give a large figure of features for accurate
identification. Fusion was considered as a consecution
between the two individual vectors in this environment.
Sprinkle-affected and low- quality X-ray images along with
good quality images were used in our trial for conducting
tests. However, the affair precision may be plant advanced,
If training and testing are performed with only named
good quality X- shaft images in an ideal situation. Still, this
doesn't represent a real- life script, where the image
database would be a blend of both good-and poor- quality
images. Thus, this approach of using different quality
images would test how well the system can reply to
similar real- life situations. A modified anisotropic
prolixity filtering fashion was employed to remove
multiplicative patch noise from the test images. The
application of these ways could effectively overcome the
limitations in input image quality. Next, the point
extraction was carried out on the test images. Eventually,
the CNN classifier performed a group of X-ray images to
identify whether it was COVID-19 or not.
• CNN RGB Matrix
Convolutional neural networks are composed of multiple
layers of artificial neurons. Artificial neurons, a rough
reproduction of their natural counterparts, are fine
functions that calculate the weighted sum of multiple
inputs and labors an activation value. When you input an
image in a ConvNet, each sub-caste generates several
activation functions that are passed on to the coming sub-
caste.
The first sub-caste generally extracts introductory
features similar as vertical or slant edges. This affair is
passed on to the coming sub-caste which detects more
complex features similar as corners or combinational
edges. As we move deeper into the network it can identify
indeed more complex features similar as objects, faces, etc.
Grounded on the activation chart of the final complication
sub-caste, the bracket sub-caste labors a set of confidence
scores (values between 0 and 1) that specify how likely
the image is to belong to a “class.” For case, if you have a
ConvNet that detects cats, dogs, and horses, the product of
the final sub-caste is the possibility that the input image
contains any of those animals
⮚ MAX-POOLING
Before we go to the working of CNN’s let’s cover the basics
similar as what's an image and how is it represented. An

RGB image is nothing but a matrix of pixel values having
three planes whereas a grayscale image is the same but it
has a single plane. Take a look at this image to understand
more
Analogous to the Convolutional Layer, the Pooling sub-
caste is responsible for reducing the spatial size of the
Convolved Point. This is to drop the computational power
needed to reuse the data by reducing the confines. There
are two types of pooling average pooling and max pooling.
I ’ve only had experience with Max Pooling sounded
problem free.
So, what we do in Max Pooling is we find the maximum
value of a pixel from a portion of the image covered by the
kernel. Max Pooling also performs as a Noise Suppressant.
It discards the noisy activations altogether and also
performs denoising along with dimensionality decrement.
On the other hand, Average Pooling returns the normal of
all the values from the portion of the image covered by the
Kernel. Average Pooling simply performs dimensionality
reduction as a noise suppressing medium. Hence, we can
say that Max Pooling performs a lot better than Average
Pooling.
⮚ DATA AUGMENTATION
The data augmentation way examined in this study have
been used in the literature for detecting COVID-19. Data
augmentation procedures are naturally arbitrary and their
justification is grounded upon empirical considerations
(i.e., model performance) rather than fixed clinical
considerations. Then, we will examine different data
augmentation styles used lately in the literature. Our ideal
is to understand the impact of data augmentation and
more understand whether one form of data augmentation
is more useful than another. Four data augmentation
styles proposed by Yoo etal., Nishio etal., Ahuja etal., and
Zhang etal., enforced as follows
• Data Augmentation 1 This augmentation step is
proposed by Nishio etal., which includes gyration within
the range (− 15, 15), restatement in x-and y- axis within
the range (− 15, 15), vertical flipping, spanning, and shear
within the range 85 – 115.
proposed by Ahuja etal., which includes shear operation
within the range (− 30, 30), arbitrary gyration within the
range (− 90, 90), and arbitrary restatement from pixel
range (− 10, 10).
proposed by Zhang etal., which includes arbitrary gyration
(30- degree range) and vertical flipping.
proposed by Yoo etal., which includes arbitrary gyration
by 10-degree, restatement, and horizontally flipping.
5. Conclusion-
The coronavirus epidemic has stretched the healthcare
systems in every country in the world to its limit as they
had to deal with a large number of deaths. Beforehand
discovery of the COVID-19 in a briskly, easier, and cheaper
way can help in saving lives and reduce the burden on
healthcare professionals. Artificial intelligence can play a
big part in relating COVID-19 by applying image
processing ways to X-ray images. This work designed and
developed an intelligent system for the COVID-19
identification with high preciseness and minimal
complexity by combining the features uprooted by
histogram- acquainted grade (HOG) features and
convolutional neural network (CNN). Suitable point
selection and bracket are absolutely vital in the COVID-19
discovery using casket X-ray images. Chest X-ray images
were entered into the system in order to produce the
affair of the pronounced lung significant region, which was
used to identify COVID-19. The proposed feature emulsion
system showed an advanced bracket precision (99.49)
than the closeness attained by using features attained by
individual point birth ways, similar as HOG and CNN. CNN
produced the stylish bracket delicacy compared to the
other classification ways, similar as ANN, KNN and SVM.
Likewise, the proposed emulsion fashion was validated
with advanced rigor using conception and k-fold
confirmation ways
6. REFERENCES
[1] K. Elissa, “Title of paper if known,” unpublished.

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[9] WHO Coronavirus Disease (COVID-19) Dashboard.
Available online:
https://covid19.who.int/?gclid=CjwKCAjw5p_8BRBU
EiwAPpJO682JEO1UwRkSSDosfqaqGeAncQYeiEeTcn
MSFJd55I0lzYlHrvi4SxoCAeUQAvD_BwE (accessed on
15 October 2020).

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