/Chest-Xray-Version1

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CHEST XRAY VERSION 1 (OLD VERSION)

The model detect the abnormalities in chest-Xray image by using RetinaNet

This project is currently version 1 by using transfer learning from fizyr

INTRODUCTION

This project used the dataset form VinBigData to classify and localize 14 diseases in chest-Xray.

But after experiments, we decided to choose 5/14 diseases from that dataset (the reason, we have described in detail in Data_Preprocessing).

Our result after 85 epochs:

Disease Aortic enlargement Cardiomegaly ILD Pleural thickening Pulmonary fibrosis
AP 0.9751 0.9478 0.9478 0.6561 0.7104

mAP (Training): 0.8044 for 5 diseases

INSTALLATION

  1. Create virtual environment
conda create -n myenv python=3.8
conda activate myenv
  1. clone this repository
  2. Install required packages
pip install Keras_retinanet/.
pip install -r Keras_retinanet/requirements.txt

  1. In the repository, execute bash setup_data.sh for create folder and download small dataset.

  2. Download pretrain model

python config/download_model.py --dest Keras_retinanet/snapshots/pretrain_model.h5
  1. Setup
cd Keras_retinanet
python setup.py build_ext --inplace

FOR TRAINING

  1. Convert dataset to standard format
python config/convert_data.py --Dataset_small/dataset_after_processing_small.csv --dest Keras_retinanet
  1. Change directory in the Keras_retinanet folder and training
cd Keras_retinanet
python keras_retinanet/bin/train.py --freeze-backbone  --workers 0 --weights snapshots/pretrain_model.h5 --backbone "resnet101"  --lr 0.00002  --batch-size 6 --steps 20  --image-min-side 900 --image-max-side 900 --epochs 2 csv annotation_5_classes.csv  classes_5.csv --val-annotations annotation_5_classes.csv

FOR DEPLOYING STREAMLIT WEB APPLICATION

  1. Run streamlit
streamlit run streamlit.py

Start to enjoy