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This project is a tool to build CheXNet-like models, written in Keras for TB X-ray detection

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DELAX-TB

DELAX-TB is a deep learning tool and architecture for detection of TB in x-ray scans. It's based on CheXnet and the source is based off of Bruce Chou's work at https://github.com/brucechou1983/CheXNet-Keras

Refer to the README at https://github.com/brucechou1983/CheXNet-Keras for the initial setup.

ChexNet is a deep learning algorithm that can detect and localize 14 kinds of diseases from chest X-ray images. As described in the paper, a 121-layer densely connected convolutional neural network is trained on ChestX-ray14 dataset, which contains 112,1\20 frontal view X-ray images from 30,805 unique patients. The result is so good that it surpasses the performance of practicing radiologists.

In this project:

  1. We took the original model from Bruce's source and loaded the saved "best" weights he shared
  2. Employing transfer learning, we replaced the last layer with a 3 node dense layer for tb,normal,uncertain categories of x-ray scans
  3. Re-trained the model with our samples
  4. Saved our new model
  5. Converted the saved model to tensorflowjs
  6. Used this tfjs model.json and bins for the DELAX-TB prototype

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MIT

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This project is a tool to build CheXNet-like models, written in Keras for TB X-ray detection

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