Classification of Chest X-ray Pathologies in Pediatric Patients Using Deep Convolutional Neural Networks

This repository contains the training code for our paper entitled "Classification of Chest X-ray Pathologies in Pediatric Patients Using Deep Convolutional Neural Networks", which was submitted and under review by Medical Imaging with Deep Learning 2021 (MIDL2021).

What the code include?

• If you want to train yourself from scratch, we provide training and test the footwork code. In addition, we provide complete training courses.

Train the model by yourself

• Data preparation

We gave you the example file, which is in the folder config/train.csv

You can follow it and write its path to config/example.json

• If you want to train the model, please run the commands below (you can change the configuration in config file, which is in the folder config/example.json):

pip install -r requirements.txt
python train.py --config ./config/example.json

• If you want to test your model, please run the command (you can also change the configuration in config file, which is in the folder config/test_config.json):

python test.py --config ./config/test_config.json

The performance of the proposed method

Classifier		AUROC	Sensitivity	Specificity	F1 score
DenseNet-121	val	0.748 (0.726-0.768)	0.732 (0.699-0.773)	0.655 (0.637-0.670)	0.286 (0.269-0.303)
	test	0.733 (0.712-0.753)	0.689 (0.653-0.730)	0.631 (0.614-0.645)	0.268 (0.253-0.283)
DenseNet-169	val	0.748 (0.726-0.769)	0.761 (0.723-0.796)	0.634 (0.618-0.650)	0.285 (0.268-0.300)
	test	0.739 (0.719-0.758)	0.733 (0.697-0.767)	0.625 (0.609-0.641)	0.274 (0.259-0.288)
ResNet-101	val	0.746 (0.724-0.767)	0.707 (0.667-0.746)	0.690 (0.674-0.707)	0.288 (0.271-0.305)
	test	0.729 (0.709-0.751)	0.672 (0.632-0.709)	0.669 (0.653-0.687)	0.273 (0.256-0.287)
DenseNet-121+Transfer	val	0.781 (0.761-0.800)	0.753 (0.718-0.786)	0.686 (0.670-0.702)	0.305 (0.287-0.321)
	test	0.762 (0.742-0.782)	0.742 (0.706-0.776)	0.652 (0.636-0.668)	0.287 (0.272-0.301)
DenseNet-169+Transfer	val	0.762 (0.740-0.783)	0.741 (0.701-0.780)	0.676 (0.658-0.695)	0.306 (0.290-0.322)
	test	0.762 (0.742-0.782),	0.742 (0.703-0.780)	0.644 (0.625-0.663)	0.297 (0.283-0.310)
ResNet-101+Transfer	val	0.766 (0.746-0.786)	0.729 (0.688-0.769)	0.690 (0.674-0.706)	0.307 (0.289-0.324)
	test	0.763 (0.743-0.783)	0.712 (0.671-0.752)	0.665 (0.650-0.681)	0.298 (0.282-0.313)
Ensemble	val	0.795 (0.776-0.813)	0.752 (0.712-0.790)	0.711 (0.695-0.726)	0.321 (0.304-0.338)
	test	0.786 (0.767-0.804)	0.742 (0.704-0.778)	0.680 (0.663-0.696)	0.306 (0.291-0.320)