The document outlines a project for COVID-19 detection using x-ray image processing, which employs a deep learning model (Inception V3) and a support vector machine (SVM) classifier. The system aims to provide an alternative to standard PCR testing methods by allowing for self-detection through a user-friendly GUI. Performance evaluation indicates an accuracy of 96.3% for the model based on confusion matrix metrics.

2. Supervisor:
Mam, Meher-Un-Nisa

3. Group Members:
⮚Saad Ali Zafar
⮚Sajid

4. Covid Detection
Via X-ray Image Processing

5. ⮚ Covid is spreading rapidly throughout
the world, best standard methods for
detecting corona is PCR test, however,
the facilities for this are limited .
⮚ By using Deep Learning Model
Inception V3,a system is developed to
give a self detection method to save
crucial resources.
.
Introduction

6. ⮚ To provide tool for Covid
detection based on X-ray Image
Processing.
⮚ Collection and analysis of data
set.
⮚ Purpose and train deep learning
algorithm for Covid detection.
⮚ Performance evaluation of
proposed model.
Project
Goal
1

7. METHODOLOGY

8. 8
METHODOLOGY
⮚ Data sets were obtained from Kegel.
⮚ Feature extracting algorithm INCEPTION V3 , a Deep Learning Algorithm
is used.
⮚ Extracted features/Labels will be feed to a SVM model
⮚ Classification Model, SVM(Support Vector Machine) will save all the labels
of training images, will provide offline service instead of retraining the
system every time.
⮚ A GUI is build that helps user to upload respective image.
⮚ In that very same GUI, result box displays the predicted result.

9. X-ray Image of Covid & Normal Patient
Above are the X-rays for pneumonia and Covid positive patient. For a normal
eye it seems very similar to rest of the chest infection diseases. A proper
expert body is required to distinguish them for the Covid positive X-ray.

10. IMPLEMENTATION
⮚We have used MATLAB2021B
⮚GUI
App Designer for the GUI

11. FINAL DESIGN GUI

12. TRAINING OF DATASET

13. TESTING OF DATASET

14. DEEP LEARNING
MODEL

15. INCEPTION V3
⮚ The Inception V3 is a deep learning model based on Convolutional Neural
Networks, which is used for image classification. The inception V3 is a superior
version of the basic model Inception V1 which was introduced as GoogLeNet in
2014. As the name suggests it was developed by a team at Google.
⮚ Inception V3 is a Convolution Neural Network for assisting in image analysis
and object detection. It extracted features from all images using filter, pooling ,
convolution to get maximum labels to be sent to SVM classifier.

16. SVM
⮚ Support vector machines (SVMs) are a set of supervised learning methods
used for classification, regression and outliers detection. The advantages of
support vector machines are: Effective in high dimensional spaces. Still
effective in cases where number of dimensions is greater than the number of
samples.
⮚ The objective of the Support vector machine (SVMs) algorithm is to find the
number of features that distinctly classifies the data points. It is used to retrieve
trained data set in an offline mode in order to save computational time.

17. CONFUSION MATRIX
A confusion matrix is a technique for summarizing the performance of a
classification algorithm.

18. ACCURACY
Accuracy with the help of confusion matrices represent counts from predicted and actual
values. The output “TN” stands for True Negative which shows the number of negative
examples classified accurately. Similarly, “TP” stands for True Positive which indicates
the number of positive examples classified accurately. The term “FP” shows False
Positive value, i.e., the number of actual negative examples classified as positive; and
“FN” means a False Negative value which is the number of actual positive examples
classified as negative. One of the most commonly used metrics while performing
classification is accuracy. The accuracy of a model (through a confusion matrix) is
calculated using the given formula below.
ACCURACY = ( TP + TN )
(TP+TN+FP+FN)
102 + 134 = 236 = 0.96 X 100= 96.3%
102+3+6+4 245

19. Thank
you