  Transfer learning is a machine learning technique designed to improve
generalization performance by using pre-trained parameters obtained from other
learning tasks. For image recognition tasks, many previous studies have
reported that, when transfer learning is applied to deep neural networks,
performance improves, despite having limited training data. This paper proposes
a two-stage feature transfer learning method focusing on the recognition of
textural medical images. During the proposed method, a model is successively
trained with massive amounts of natural images, some textural images, and the
target images. We applied this method to the classification task of textural
X-ray computed tomography images of diffuse lung diseases. In our experiment,
the two-stage feature transfer achieves the best performance compared to a
from-scratch learning and a conventional single-stage feature transfer. We also
investigated the robustness of the target dataset, based on size. Two-stage
feature transfer shows better robustness than the other two learning methods.
Moreover, we analyzed the feature representations obtained from DLDs imagery
inputs for each feature transfer models using a visualization method. We showed
that the two-stage feature transfer obtains both edge and textural features of
DLDs, which does not occur in conventional single-stage feature transfer
models.
