/angel-pfizer

Deep Learning to classify respiratory sounds

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Deep Learning to Classify Respiratory Sounds

2nd place, Digital Health Challenge at the CMG Hackathon

Built a deep learning classifier to differentiate between sick and not-sick patients. Final model - 87.6% accuracy on test set

  • Melspectrogram data
  • Image Transformations (normalize mean, variance)
  • Base: VGG Model pretrained w/ ImageNet
  • Top: Densely connected network w/ Dropout
  • Optimizer: SGD w/ learning rate decay
  • 5 Frozen layers

TODO:

  • Ensemble Learning: Combine multiple models prior to densely connected block
  • Model Averages: Take models trained on different models, average final results to estimate condition