Description
Flutter TensorFlow Lite Artificial Intelligence Application Detection of Lungs Pneumonia COVID 19
Flutter TensorFlow Lite Artificial Intelligence Application Detection of Lungs Pneumonia COVID 19
Lungs Pneumonia Scanner mobile app was developed with Flutter. In terms of code structure, it was written with the principle of clean code.
The interface is simple and very easy to edit.
It can be run on both Android and iOS platforms.
It has an application structure that can be installed on Google Play Store, App Store and Huawei App Gallery platforms.
It can be easily run offline as there are no options such as database and remote connection.
Developed with a modern, simple user-friendly interface using the advantages of Flutter, Lungs Pneumonia Scanner makes it easier for you to detect Lungs Pneumonia. And in seconds!
All the steps that need to be done in detail are included in the documentation.
It should not be thought of as just an eye scanning application.
If the Tensorflow model is changed, it can also be used to detect other diseases.
After the Covid-19 epidemic, which affected the whole world, many new concepts entered our daily lives. The vocabulary of Covid-19, which started to grow with the mask, distance and hygiene trio, later incorporated concepts such as pneumonia and pneumonia into its body. My wish is for this epidemic to end before more lives are harmed. The purpose of this application is to obtain Tensorflow Lite output from a dataset containing child lung x-rays and integrate this output into the mobile application using the Flutter SDK.
Within the scope of this study, early diagnosis and leveling of lung pneumonia has been made possible by using deep learning, image processing methods and convolutional neural networks. The diagnosis protocol has been converted into a mobile application so that the diagnosis of lung pneumonia can be made easily and quickly in daily life. With the mobile application, both the diagnosis of lung pneumonia and more regular results can be obtained easily and practically.
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 drscanner file with Visual Studio Code
- Run this command on the terminal flutter pub get
- And finally flutter run
