Chest Xray image analysis using Deep Learning and exploiting Deep Transfer Learning technique for it with Tensorflow.

The maxpool-5 layer of a pretrained VGGNet-16(Deep Convolutional Neural Network) model has been used as the feature extractor here and then further trained on a 2-layer Deep neural network with SGD optimizer and Batch Normalization for classification of Normal vs Nodular Chest Xray Images.

Accuracy : 69.3333 %

openi.nlm.nih.gov has a large base of Xray,MRI, CT scan images publically available.Specifically Chest Xray Images have been scraped, Normal and Nodule labbeled images are futher extrated for this task.

The above code can be used for Deep Transfer Learning on any Image dataset to train using VggNet as the PreTrained network.

1. Download Data- the script download images and saves corresponding disease label in json format.

python scraper.py <path/to/folder/to/save/images>

2. Follow the scraper/process.ipynb notebook for Data processing and generate

• Training images folder - All images for training
• Testing images Folder - All images for testing
• Training image labels file - Pickled file with training labels
• Testing image labels file - Pickled file with testing labels

3. Extract features(CNN Codes) from the maxpool:5 layer of PreTrained CovNet(VggNet) and save them beforehand for faster training of Neural network.

   python train.py <Training images folder> <Testing image folder> <Train images codes folder > <Test images codes folder>

4. The extracted features are now used for training our 2-Layer Neural Network from scratch.The computed models are saved as tensorflow checkpoint after every Epoch.

   python train_model.py <Training images folder> <Train images codes folder> <Training image labels file> <Folder to save models>

5. Finally the saved models are used for making predictions.Confusion Matrix is used as the Performance Metrics for this classifcation task.

   models>```

1. Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation

2. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

If you want to contribute and add new feature feel free to send Pull request here :D

To report any bugs or request new features, head over to the Issues page