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

Feature Representation Analysis of Deep Convolutional Neural Network  using Two-stage Feature Transfer -An Application for Diffuse Lung Disease  Classification-