  Purpose: Image classification may be the fundamental task in imaging
artificial intelligence. We have recently shown that reinforcement learning can
achieve high accuracy for lesion localization and segmentation even with
minuscule training sets. Here, we introduce reinforcement learning for image
classification. In particular, we apply the approach to normal vs.
tumor-containing 2D MRI brain images.
  Materials and Methods: We applied multi-step image classification to allow
for combined Deep Q learning and TD(0) Q learning. We trained on a set of 30
images (15 normal and 15 tumor-containing). We tested on a separate set of 30
images (15 normal and 15 tumor-containing). For comparison, we also trained and
tested a supervised deep-learning classification network on the same set of
training and testing images.
  Results: Whereas the supervised approach quickly overfit the training data
and as expected performed poorly on the testing set (57% accuracy, just over
random guessing), the reinforcement learning approach achieved an accuracy of
100%.
  Conclusion: We have shown a proof-of-principle application of reinforcement
learning to the classification of brain tumors. We achieved perfect testing set
accuracy with a training set of merely 30 images.
