https://github.com/joyalshaji135/cnn-implementation

Automated detection of COVID-19 in real time can greatly help clinicians to handle increasing number of cases for preliminary screening. Deep CNN models trained with sufficiently large datasets may become best candidates to meet the purpose.
https://github.com/joyalshaji135/cnn-implementation

csv deep-learning deep-neural-networks jupyter-notebook keras neural-networks python3

Last synced: 26 days ago
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Automated detection of COVID-19 in real time can greatly help clinicians to handle increasing number of cases for preliminary screening. Deep CNN models trained with sufficiently large datasets may become best candidates to meet the purpose.

• Host: GitHub
• URL: https://github.com/joyalshaji135/cnn-implementation
• Owner: joyalshaji135
• Created: 2023-09-16T08:22:52.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-04-13T17:39:20.000Z (8 months ago)
• Last Synced: 2024-04-14T16:15:16.917Z (8 months ago)
• Topics: csv, deep-learning, deep-neural-networks, jupyter-notebook, keras, neural-networks, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 653 KB
• Stars: 16
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# CNN-Implementation

A total of 15,153 samples are used in this work. These samples include chest X-ray images of COVID-19, viral pneumonia, and normal cases. The entire dataset was split into train and test sets, with a ratio of 80:20 before training the model. To enhance important image features, image preprocessing and augmentation were applied before feeding the image batches to the model.

    

    Testing Result

    

    

        Test Implementation Name

        Test Accuracy

    

    

       CNN Implementation - 1

        0.8780487775802612

    

        CNN Implementation - 2

        0.9451219439506531

    

    

        Transfer_Learning_Implementation - 3

        0.957317054271698