This project is a chest X ray images muti-label classification algorithm with high performance which have potential to help radiologist diagnosis pulmonary disease better.
The baseline of this algorithm is CheXNet, I learn lots of things the from the paper which help me train my DenseNet121 well.
All the model trained on NIH ChestX-ray14 dataset, you can download the data here

• Download ChestX-ray14 dataset here, unzip all 14 xxx.tar.gz file to a single floder, run tar -xvzf *ZIPFILENAME* in terminal, ZIPFILENAME is the .tar.gz file you want to unzip.
• Modify the image path in shell/write_all.sh, image path fellowed by --dataset_dir tag.
• Change your work directory to ChestRayXNet.
• Run shell/write_all.sh to convert all 112,120 chest X ray images into .tfrecord format.
• Run shell/train_densenet_121.sh to start training a network named CheXNet. I recommend start by running train_densenet_121.sh as this script may have more additional features and less bug.
• Futher, if you want to try different networks, modify the network type in shell/train_generate.sh fellowed by --model_type tag and log directory. If you get OOM error during training, modify the batch size fellowed by --batch_size tag.
• To Visualize training procedure, run tensorbord --logdir='*LOG_DIR*' --port=6006 in terminal, LOG_DIR is the dir set after --log_dir tag in the shell script you running.
• To evaluate the model performance, run shell/eval_muti.sh, if your train different network other than DenseNet121, modify the network type and log dir in eval_muti.sh file.

1.The baseline model of this project is CheXNet published by Andrew Ng et al.
2.All network writen by slim are provided by tensorflow contributers and available on github, and related pretrained model can be find here
3.A baseline project fine-tuning from ImageNet and trained on Oxford-flower dataset, I'm searching for the github link of that project.
4.I add weight to loss function fellowing this paper published by Buda et al.
5.Two earlier research on NIH ChestX-ray14 also provide many insight to me, they can be find here and here.

This README file write in a hurry and I belive I must make some mistake, please let me know if thier is any word that made you confused.
I update this README.md file 3 days after first commit but I think it's still not good and clear enough, maybe next version would be better.

This project is writen by Ruiqi Sun(孙瑞琦）