a-abdellatif98/Predict-Lung-Disease

Predict-Lung-Disease-through-Chest-X-Ray

We obtain this repository by refactoring the code for the blog post Using Microsoft AI to Build a Lung-Disease Prediction Model using Chest X-Ray Images. This instruction aims to help newcomers build the system in a very short time.

Installation

1. Clone this repository

   git clone https://github.com/svishwa/crowdcount-mcnn.git

   We'll call the directory that you cloned PredictLungDisease ROOT

2. All essential dependencies should be installed：pickle, random, re, tqdm, cv2, numpy, pandas, sklearn, keras, tensorflow, keras_contrib, collections.counter.

Data set up

1. Download the NIH Chest X-ray Dataset from here:
   https://nihcc.app.box.com/v/ChestXray-NIHCC.
   You need to get all the image files (all the files under images folder in NIH Dataset), Data_Entry_2017.csv file, as well as the Bounding Box data BBox_List_2017.csv.

2. Create Directory

   mkdir ROOT/azure-share/chestxray/data/ChestX-ray8/ChestXray-NIHCC
   mkdir ROOT/azure-share/chestxray/data/ChestX-ray8/ChestXray-NIHCC_other

3. Save all images under ROOT/azure-share/chestxray/data/ChestX-ray8/ChestXray-NIHCC

4. Save Data_Entry_2017.csv and BBox_List_2017.csv under ROOT/azure-share/chestxray/data/ChestX-ray8/ChestXray-NIHCC_other

5. Process the Data

   mkdir ROOT/azure-share/chestxray/output/data_partitions

   Run 000_preprocess.py to create *.pickle files under this directory

Test

1. We have provided the pretrained-model azure_chest_xray_14_weights_712split_epoch_054_val_loss_191.2588.hdf5 under ROOT/azure-share/chestxray/output/fully_trained_models. You can also download it separately from here.

2. Run 020_evaluate.py and it will create weights_only_azure_chest_xray_14_weights_712split_epoch_054_val_loss_191.2588.hdf5 saving weights of the pretrained-model under the same directory.

3. Below is the result showing the AUC score of all the 14 diseases:

   Disease	Our AUC Score	Stanford AUC Score	Delta
   Atelectasis	0.822334	0.8094	-0.012934
   Cardiomegaly	0.933610	0.9248	-0.008810
   Effusion	0.882471	0.8638	-0.018671
   Infiltration	0.744504	0.7345	-0.010004
   Mass	0.858467	0.8676	0.009133
   Nodule	0.784230	0.7802	-0.004030
   Pneumonia	0.800054	0.7680	-0.032054
   Pneumothorax	0.829764	0.8887	0.058936
   Consolidation	0.811969	0.7901	-0.021869
   Edema	0.894102	0.8878	-0.006302
   Emphysema	0.847477	0.9371	0.089623
   Fibrosis	0.882602	0.8047	-0.077902
   Pleural Thickening	1.000000	0.8062	-0.193800
   Hernia	0.916610	0.9164	-0.000210

Visualization

1. Create Folder Test

   mkdir ROOT/azure-share/chestxray/data/ChestX-ray8/test_images

   Copy any number of images under ChestXray-NIHCC to test_images and resize them to 224x224 pixels.

2. Run 004_cam_simple.py and it will output a Class Activation Map(CAM). The CAM lets us see which regions in the image were relevant to this class.

   

Referenced Paper

• Baseline result: https://arxiv.org/abs/1705.02315
• Image Localization: http://arxiv.org/abs/1512.04150
• The original chexnet paper mentioned in StanfordML website as well as their paper.
• http://cs231n.stanford.edu/reports/2017/pdfs/527.pdf for pre-processing the data
• https://arxiv.org/abs/1711.08760 for some other thoughts on the model architecture and the relationship between different diseases

Notes

Please contact yanhaotian@bupt.edu.cn if you have any problem.