Transfer Learning for Small-scale Data Classification Using CNN Filter Replacement

Recently, object recognition using CNN is widespread. Still, medical images do not have a sufficient number of images because they require the doctor's findings in the training dataset. On such a small-scale dataset, there is a problem that CNN cannot realize enough high recognition accuracy. As a solution to this problem, there is a method called transfer learning that reuses the weights learned on a large dataset. In addition, there is research on a method of pruning parameters unimportant for the target task during transfer learning. In this study, after transfer learning is performed, the convolution filter is evaluated using pruning, and the low evaluation filter is replaced with the high evaluation filter. In order to confirm the usefulness of the proposed method in recognition accuracy, we compare it with the three methods, i.e., transfer learning only, pruning, and initializing the filter. As a result, we were able to obtain a high recognition accuracy compared to other methods. We confirmed that CNN might be affected by replacing the filter in object recognition of small-scale datasets.