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Xvision -анализируем ренгеновские снимки с помощью Deep learning !

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README.md

Xvision

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.

Nodular vs Normal Chest Xray

Some specifications

PropertyValues
Pretrained ModelVggNet-16
Optimizer usedstochastic gradient descent(SGD)
Learning rate0.01
Mini Batch Size20
Epochs20
2 Layers512x512
GPU trained onNvidia GEFORCE 920M

Evaluation

Confusion Matrix and Training Error Graph

NormalNodule
Precision0.77551020.55555556
Recall0.760.57692308

Accuracy : 69.3333 %

DataSet

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.

How to use ?

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

Steps to follow

  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.

    python test_model.py <Testing images folder> <Test images codes folder> <Testing image labels file> <Folder with saved models>

Some Predictions

Alt text

References

  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

Contribute

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



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