We present the use of an error correcting autoencoder stage to a convolutional neural network model as a means of improving image based automatic Positive Airway Pressure (PAP) mask sizing accuracy. A single convolutional layer neural network was pre-trained using MUCT dataset and transfer learning was applied to mitigate against the relatively small custom dataset. The base model was then augmented with an additional error correcting autoencoder and trained against the custom dataset. The presented model increased PAP sizing accuracy against the baseline by 15.3% while reducing overfitting.
Humans Image Processing, Computer-Assisted