Dataset Information

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

Demo Link: