The document presents two proposed models for detecting COVID-19 from X-ray images using artificial intelligence. The first model uses deep learning with data augmentation and preprocessing of X-ray images, evaluating different optimizers and activation functions. It achieves the best accuracy of 95.2% using Adam optimizer and LeakyReLU activation. The second model performs feature fusion of GLCM and CNN features, achieving 96.1% accuracy with a batch size of 128. Previous work on COVID-19 classification using methods like SVM and deep learning are also discussed. Future work can focus on expanding datasets and interpreting model results.

1. Development of Detection Algorithms of COVID-
19 Virus Using Artificial Intelligence
‫الذكاء‬ ‫باستخدام‬ ‫كورونا‬ ‫فيروس‬ ‫اكتشاف‬ ‫خوارزميات‬ ‫تطوير‬
‫االصطناعي‬
Under Supervision of
Prof. Mohamed Abdel-Azim Mohamed
Dr. Eman Mahmoud Abd El-halim
Prepared by
Eng. Abeer Abd-Elhai Abd-Elhamid
Mansoura University - Faculty Of Engineering
Electronics and Communications Engineering Department
2022

2.  Virus:
Is an infectious agent of small size and simple composition that can multiply only in living cells
of animals, plants, or bacteria.
 The name is from a Latin word meaning “slimy liquid” or “poison.”
 Virus structure:
1- A virus particle is made up of genetic material housed inside a protein shell, or capsid.
2- The genetic material, or genome, of a virus may consist of single-stranded or double-
stranded DNA or RNA and may be linear or circular in form.
 Properties of Viruses:
1- They are non-cellular
organisms, which is
enclosed in a protective
envelope.
2- They are considered
both as living and non-
living things because,
they are inactive when
they are present
outside of host cells but
become active within
host cells.
3- These viruses cause
several infections and
reproduce within the
host cell by using the
enzymes and raw
materials.

3. Classification of Viruses:
1- Classification based on the presence of nucleic acid:
a) DNA virus:
The virus, having DNA as its genetic material.
b) RNA virus
The virus, having RNA as its genetic material.

4. 2- Classification based on the structure or symmetry:
a)Complex virus.
b)Radial symmetry virus.
c) Cubical or icosahedral symmetry shaped virus.
d) Rod or Spiral shaped or helical symmetry virus.
3- Classification based on the host range:
a) Animal viruses
These viruses infect by invading the cells of animals, like influenza virus.
b) Plant viruses
These viruses infect plants by invading the plant cells.
c) Bacteriophage
The virus which infects bacterial cells is known as bacteriophage

5. 4- Viruses Classification based on the mode of transmission:
Airborne
infections
Through the air, like Swine flu
Faecal oral
route
Through the contaminated water or food, like Hepatitis A virus.
Transfusion
-
transmitted
infections
Through the blood transfusion, like Hepatitis B virus.
Zoonoses Through the biting of infected animals, birds, and insects like, Rabies virus.

7.  is an infectious disease caused by severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2).
 The disease was first identified in December 2019 in Wuhan, the
capital of China's Hubei province, and has since spread globally.
 its transmission from a human to a human being.
 Problem Statement:

9. COVID-19 Databases:
CT Xray
1- Image based databases 2- Numerical based databases
PCR Laboratory tests

10.  As a first-line screening tool, RT-PCR is the gold standard, but it has discovered that the test result
sensitivity ranges between 50% to 62%.
 As a result, medical imaging modalities such as Chest X-Ray (CXR) and Computed Tomography (CT) can play
a significant role in COVID-19 detection.
 CXR tests are fast operating speed, low cost, and ease of use for radiologists.
COVID-19 Diagnosis methods:

11.  Related Work:

12. Challenges:
Data
imbalance
Handling of
huge image
size
Limited
available
datasets

14. Machine Learning
 It is a branch of artificial intelligence based on the idea that systems can learn from data.

15.  Difference between machine learning and deep learning:

16. Our Contribution
Results
Classification
Pre-Processing
X-Ray Images
Enhancement
Data Augmentation
Normal
COVID-19
Pneumonia
The First proposed Model Based on DL
The Seconed Proposed Model Based on Feature
Fusion Method

17. Database: Xray based images
Dataset: Abeer’s kaggle
 The used dataset in our proposed models is gathered from “Kaggle” website.
1
No.of Images
Class
1371
COVID-19
1751
Normal
4273
Pneumonia
7395
Total
The First Proposed Model Based on Deep Learning
The Dataset was splitted into 80%:10%:10% for Training, Test, and Validation
respectively.

18. Pre-
Processing

19.  Pre-Processing:
 After capturing the X-Ray images, we are applying the preprocessing
techniques on digital images by performing operations on an image in order to
improve it or extract useful information from it.
Image Enhancement

20.  We initially employed image normalization.
Image Pre-processing
 The histogram equalization was applied to enhance the image contrast.
 After histogram equalization, images are resized to 200x200 before training
begins.
 Some transformations are applied to the images includes rotation by 25
degree, zoom range equal 0.2 and fill-mode set to nearest.
 The data augmentation used to improve our model's generalization ability
because it mitigates overfitting by adding variations to the dataset.
Data Augmentation

21. Training &
Classification

22. Training & Classification
A DL model was implemented based on the TL
concept using Xception pre-trained model.
 Xception
 It is a CNN architecture that consists of a linear stack of
depth-wise separable convolution layers with residual
connections.
 The feature extraction base is formed by 36 convolutional
layers that are formulated into 14 modules with outlined
linear residual connections.
 The separable convolution layer has fewer parameters and a
lower computational cost than a traditional convolutional
layer.

23.  After importing Xception model from Keras, we have some additional layers in
our structure ) GAP, Flatten, Activation, Dropout, and Softmax) layers.
The performance of the proposed model depends on different optimizers and different
activation functions.

24. Table1: Summary of the parameter settings of the proposed structure.

25. Evaluating The Performance
For evaluating the performance on test set, four evaluation metrics accuracy, precision, recall,
F1- score and are derived from the confusion matrix. The formulas for these metrics are given below

26. Precision Refers to the closeness of the measurements to each
other.
Recall Is the percentage of total relevant results correctly classified
by the model.
Evaluation Metrics
AUC-Curve
Illustrates how well the model performs by discriminating
between classes.

27. Results

28. System performance Evaluation with different optimizers
1
LeakyRelu activation
function(0.1)
ELU activation
function(0.2)

29.  System performance with LeakyRelu activation function:
Overall evaluation of the proposed model with
different optimizers and the LeakyReLU
activation function (α= 0.1).
Comparative analysis of each class and overall accuracy
using different optimizers with the
LeakyReLU activation function set at α = 0.1.

30. Confusion matrices of the proposed model with
different optimizers using the LeakyReLU
activation function set at (α = 0.1). (a) AdaGrad,
(b) AdaDelta, (c) SGD, (d) Adam, (e) AdaMax,
and
(f) RMSprop.
ROCs of the proposed model with different optimizers
using the LeakyReLU
activation function set at (α = 0.1). (a) AdaGrad, (b)
AdaDelta, (c) SGD, (d) Adam, (e) AdaMax, and
(f) RMSprop.

31.  System performance with ELU activation function:
Overall evaluation of the proposed model with different
optimizers and ELU activation
function (α = 0.2).
Comparative analysis of each class and overall
accuracy using different optimizers with the
ELU activation function set at α = 0.2.

32. Confusion matrices of the proposed model using
the ELU activation function (α = 0.2) with
different optimizers. (a) AdaGrad, (b) AdaDelta, (c)
SGD, (d) RMSprop, (e) Adam, and (f) AdaMax.
ROCs of the proposed model using the ELU activation
function (α = 0.2) with
different optimizers. (a) AdaGrad, (b) AdaDelta, (c) SGD,
(d) RMSprop, (e) Adam, and (f) AdaMax.

33.  System performance with pre-trained models:
Confusion matrices of the pre-trained models with
learning rate η = 0.1, ReLU activation
function, and Adam optimizer. (a) VGG16, (b) Xception,
(c) DenseNet121, (d) InceptionV3, (e)
ResNet50, and (f) MobilNet.
Comparative accuracy of our model with other CNN
models. Learning rate h = 0.1, ReLU
activation function, and Adam optimizer for all pre-trained
models.

34. ROCs of the pre-trained models with learning rate η = 0.1, ReLU activation
function, and Adam optimizer. (a) VGG16, (b) Xception, (c) DenseNet121, (d) InceptionV3, (e)
ResNet50, and (f) MobilNet.

35. Previous works for COVID-19 classification. Here, MCSVM, CSEN, ML, TL, CV, and ACC
stand for multi-class support vector machine, convolution support estimation network, machine
learning, transfer learning, cross-validation, and accuracy, respectively.

36. The Second Proposed Model Based on Feature Fusion
Technique
1
Feature extraction:
 Choosing the most informative subset of features, and removing as many irrelevant and redundant features
as possible.
 Combining the existing feature set into a smaller set of new, more informative features.
 The second proposed model based on making a fusion between GLCM and CNN.

37. The Second Proposed Model Based on Feature Fusion
Technique
1
 GLCM produces a square matrix with the same dimension as the number of grey levels in the image.
GLCM and feature extraction:

38. CNN architecture to extract the high-level features:
MLP Parameters
CNN Parameters

39. Dataset:
 The used dataset in our proposed models is gathered from “Kaggle” website.
1
No.of Images
Class
579
COVID-19
1585
Normal
3856
Pneumonia
6020
Total
The Second Proposed Model Based on Feature Fusion
Technique
The training used 4725 images of the dataset splitted into 75%:25% for training
and validation, while 1288 images used for test.
Parameters and its values

40. Results

41. Overall evaluation of the proposed model with different
batch size.
 System performance with different batch size:

42. Confusion Matrices of the proposed model with different
batch size
(a) BS=8, (b) BS=32, (c) BS=64, (d) BS=100, (e) BS=128,
and (f) BS=150.
Previous works for COVID-19 classification. Here
SVM, DTL,
DRENet , MTL stand for support vector machine, deep
transfer learning, Details
Relation Extraction neural network, deep learning, and
multi-task learning
model respectively.

43. Future work

44. Other Research Points
2- Expect to focus on the use of other techniques such as IRCNN,
Multi-Criteria Decision Analysis, and Artificial Intelligence powered Internet of
Things applications, can detect COVID-19 with simplicity.
1- Extend the systems ability to fuse multi-input data, including
other imaging modality (i.e., chest CT) and clinical markers.
3- Create a mobile application for COVID-19 detection based on the cough sound of the patient.

45. List of Publications
1) Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using
Deep Learning Algorithms (Published in applied sciences journal).
2) COVID-19 Diagnosis from Chest X-Ray Scans Using Feature Fusion Strategy
(under editing).
3) Review of Artificial Intelligence Techniques for Classification and Detection
of COVID-19 (under editing).