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Flutter TensorFlow Lite Artificial Intelligence Application Detection of Lungs Pneumonia COVID 19

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Dataset Information

Tag Number | Tag
0

Healthy

1

Patient

Steps Taken

  • All images are cropped and resized using the resize script and pre-processing script.
  • Images without disease were projected using the rotation script; Images with disease were reflected and rotated 90, 120, 180 and 270 degrees.
  • After rotating and reflecting with and without disease, the class imbalance has been resolved and detected several thousand images have disease.
  • In total, there are 5000 images processed by the neural network.
  • All images were converted to NumPy Arrays using the conversion script. NumPy Arrays combined images and tags in an array and send the images to CNN.
  • The model was created by using the TensorFlow and Keras libraries. For CNN, encoding was done by using anaconda as IDE and Jupyter Notepad within anaconda.
  • The pictures are tagged and parsed the pictures used to train them in two different sequences according to the labelling.
  • The pictures were then brought to a fixed size (255*255) by grayscale method .
  • The images are then passed through CNN and are called learning.
  • The trained model can be saved and then tested with pictures.

How to use?

  • Open lungs_pneumonia file with Visual Studio Code
  • Run this command on the terminal flutter pub get
  • And finally flutter run

Information Schema

COVID19 COVID19

Demo Link: https://drive.google.com/drive/folders/1nC0wXjUVS8VDBh0_Diz6U1LBGSe0MQY0?usp=sharing


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