Pneumonia Detection

This repository contains code for pneumonia detection using X-ray images of the lungs.

Link for the dataset

Link to the trained models

Instructions

• Install required packages from requirements.txt
• You can download, extract and move the data according to the label using data.py
  • pip install kaggle
  • Download Kaggle API key from your Kaggle Account. Go to www.kaggle.com -> My Account -> Create New API token
  • Place the file in on your home directory $(HOME)/.kaggle/kaggle.json.
  • Kaggle module will look for this token at ~/.kaggle/kaggle.json on Linux, OSX, and other UNIX-based operating systems, and at C:\Users\<windows-username>\.kaggle\kaggle.json on Windows.
  • execute python data.py
• Run main.py to train from the dataset. For example, python main.py --base_model {base_model} --optimizer {optimizer} --learning_rate {learning_rate} --batch_size {batch_size} --epoch {epoch} --colab
  • To change pretrained base model, give input while initializing the model object. Use values from
    • ResNet18
    • ResNet34
    • ResNet50
    • ResNet101
    • ResNet152
    • Alexnet
    • VGG11
    • VGG13
    • VGG16
    • VGG19
    • GoogleNet
    • Inception
  • If using colab for training, mount the drive and use --colab to save the files in the drive
  • To change optimizers, use one of the following
    • Adam
    • SGD
    • RMSprop
    • Adagrad
    • Adadelta
• To test new dataset, run test.py with directory path and base model with option given above. Make sure that model is trained on those models first.
  • Example, python3 test.py "./data/Corona_Classification_data/test/" Inception
• To generate a Class Activation Map from a trained model, after training it, use CAM.py like python3 CAM.py {Model_Name} {Path_to_Image}
  • Example, python3 CAM.py Inception "./data/abc.jpg"
  • This will save the output as CAM_{Model_Name}.jpg in the current directory