diagnosis of covid 19

1. CoviFast
Classification of covid_19 from chest x-ray images

2. The new coronavirus (COVID-19) is an
acute, deadly disease that originated in
December 2019 and spread globally from
Wuhan Province, China. The epidemic of
COVID-19 has been of great concern
to the medical community because no
efficient cure has yet been found.
Real-time reverse transcription polymerase
chain reaction(R T -PCR) test has been
described by the World Health Organization
(WHO) .
Introduction

3. Problem
The spread of the Corona virus in the world,
its transmission from a human to a human
being , and the inability of everyone to
perform a diagnostic scan and resort to
chest rays to detect the presence of
pneumonia or the presence of the Corona
virus.

4. 1
1
Spread of the virus

5. Covid-19
map
183 M
Globally
01
282 K
Egypt
02
5347
Egypt (last 14 days)
03

6. Related
Work

7. Related
Work

9. Our contribution
Results
Classification
Pre-Processing
X-Ray
Normal
Covid
ResNet-50
MobileNetV2
Segmentation
Enhancement
Stacking
VGG-16
Pneumonia

10. 1
1
Dataset
The dataset used in our project is gathered from multiple
sources due to the scarce nature of approved COVID-
19 datasets.

11. Pre-
Processing

12. Pre-Processing
After capturing the X-Ray images, we are applying
the preprocessing techniques on digital images
like RGB to Gray scale conversion and used
appropriate filtering techniques.

13. Image Enhancement
The first instance of the input X-ray scan is enhanced
using the techniques explained below :
1) Median Filter
2)Fuzzy Histogram Hyperonization.

14. Image Segmentation
The second instance of the input X-ray scan
1)U-Net Model
2)Image Filtering for segmentation

15. Stacking and Augmentation
- Image stacking: The purpose of Image Stacking is to
move the individual segment images so that they fall
precisely on top of each other.
- inbalancing..The original dataset (without augmentation)
contains only 554
chest X-ray scans of COVID-19

16. Training &
Classification

17. Training &
Classification
In this study, deep learning was used for classifying images into
COVID-19 or Normal categories. An ensembled model was built by
concatenating the features of three different Convolutional Neural
Networks

18. VGG-16
VGG-16 is a simple 16 layered
Convolutional Neural Network . It has
convolutional filter of size 3 × 3 and
pooling filter of size 2 × 2.

19. ResNet50
ResNet-50 is a residual network with 50 layers
stacked together and has shortcut or skip
connections. The skip connection passes the
same information in the network. Passing the
same information allows that the model does not
degrade by losing the information

20. MobileNetV2
MobileNetV2 is a very light, low-latency and low-
powered model which requires very low
hardware setup for training a model. It has linear
layers for linear bottleneck between the layers
which prevents non-linearities from destroying
the information

21. Ensemble Model
The flatten output features from these sub-models are then concatenated
to make an ensemble of these three models. A meta learner is created
for classifying these features in one of the three categories .

22. 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

23. Dataset &
Results

24. 1
1
Dataset
The dataset used in our project is gathered from multiple
sources due to the scarce nature of approved COVID-
19 datasets.

25. 1
1
Result

26. System
Analysis

27. Use Case

28. Sequence
diagram

29. Activity
diagram

30. Class
diagram

31. Future work

32. THANK YOU