This repository provides source code of CLI app for training and inferring lung injury detection model. The model is based on U-Net architecture implemented with pytorch framework for python.

The project depends on python ==3.10.*

Install from PyPI:

pip install LungDamageDetectionCLI

Install from repo:

git clone <repo-name> && cd <project-dir>
pdm install

Run CLI app from project dir

pdm run ldd-cli SUBCOMMAND

This app supports these subcommands:

• train - run model training
• test - run model test
• inference - detect injuries

You may copy .env.example and rename it into .env

Environment Variables

PATH_TO_DATA=  # directory containing dataset 
DCM_DIR=  # directory inside PATH_TO_DATA containing dcm files
PNG_DIR=  # directory inside PATH_TO_DATA containing png files
TRAIN_SET=  # number of images for model training
TEST_SET=  # number of images for model test
VAL_SET=  # number of images for model validation
EPOCHS=  # number of training epochs
LR=  # learning rate
BATCH_SIZE=  # size of batch
IMG_SIZE=512 # width and height of images in dataset
BACKBONE=resnet101 # name of backbone model
SAVE_PATH=  # path of trained model state dict
OUT_PATH=  # directory to save inference results
INPUT_PATH=  # dcm file or directory of dcm files for model inference
USE_AUGMENTATION=false # dataset is loaded with augmentation if true
SEED=5 # seed for numpy.random.seed

or pass desired values through command line args (names of args are case insensitive).

Supported backbone names:

• resnet18
• resnet34
• resnet50
• resnet101
• resnet152
• vgg16
• vgg19
• densenet121
• densenet161
• densenet169
• densenet201

All code is placed in src directory.

To train model place .dcm files of CT of lungs in PATH_TO_DATA/DCM_DIR and .png masks in PATH_TO_DATA/PNG_DIR then set TRAIN_SET, VAL_SET and TEST_SET variables according to your dataset size. App saves state dict of model every epoch. If SAVE_PATH file exists model will be loaded using it when the app is restarted.

If USE_AUGMENTATION is true some images will be rotated, cropped and flipped.

After model test trained model metrics are printed.

Metrics

• precision
• recall
• f1 score
• jaccard coefficient
• dice coefficient

To inference model you have to provide path of dcm file or path of directory with several dcm files into INPUT_PATH.

Result png files are placed in OUTPUT_PATH. Png files have same name as dcms.

Lung injury detection CLI app is MIT licensed