I am interested in interpreting X ray images using deep learning. Particularly Chest X ray images. I am using Montgomery TB dataset and Shenzhen TB dataset for this experiment. I am trying to predict whether an X ray image is normal or has TB (nodule) indicaitons.
I tried fine-tuning the ResNet models from lesson 1. So far the performance is not that great. ResNet on untreated images (converting gray to 3 channels) gives me accuracy in range of 68-70%. I tried histogram equalisation on X ray image and accuracy was in the range of 74-75%. With histogram equalisation as one of the channels of the image, the accuracy was in the range of 77-82%. Accuracies on validation set is comparable to training set accuracies.
- Are there any data preparation methods which are well suited for X ray images?
- Are there any different models which I should be trying?
- Are Imagenet models capable of extracting patterns from X ray images? If not, should I build my own model rather than using Imagenet models?
- The total set of images are 800 in number. Should I be doing data augmentation? What sort augmentation helps? (I can think of flipping the image on the horizontal axis and small random zooming in.)
Thanks in advance.