Ce diaporama a bien été signalé.
Le téléchargement de votre SlideShare est en cours. ×

COVID-19 PowerPoint.pptx

Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Publicité
Chargement dans…3
×

Consultez-les par la suite

1 sur 32 Publicité
Publicité

Plus De Contenu Connexe

Publicité

COVID-19 PowerPoint.pptx

  1. 1. CoviFast Classification of covid_19 from chest x-ray images
  2. 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. 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. 4. 1 1 Spread of the virus
  5. 5. Covid-19 map 183 M Globally 01 282 K Egypt 02 5347 Egypt (last 14 days) 03
  6. 6. Related Work
  7. 7. Related Work
  8. 8. Our contribution Results Classification Pre-Processing X-Ray Normal Covid ResNet-50 MobileNetV2 Segmentation Enhancement Stacking VGG-16 Pneumonia
  9. 9. 1 1 Dataset The dataset used in our project is gathered from multiple sources due to the scarce nature of approved COVID- 19 datasets.
  10. 10. Pre- Processing
  11. 11. 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.
  12. 12. 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.
  13. 13. Image Segmentation The second instance of the input X-ray scan 1)U-Net Model 2)Image Filtering for segmentation
  14. 14. 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
  15. 15. Training & Classification
  16. 16. 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
  17. 17. 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.
  18. 18. 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
  19. 19. 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
  20. 20. 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 .
  21. 21. 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
  22. 22. Dataset & Results
  23. 23. 1 1 Dataset The dataset used in our project is gathered from multiple sources due to the scarce nature of approved COVID- 19 datasets.
  24. 24. 1 1 Result
  25. 25. System Analysis
  26. 26. Use Case
  27. 27. Sequence diagram
  28. 28. Activity diagram
  29. 29. Class diagram
  30. 30. Future work
  31. 31. THANK YOU

×