Analysis of the effects of transfer learning on opacity classification of diffuse lung diseases using convolutional neural network

Abstract

Research on Computer-Aided Diagnosis (CAD), which discriminates the presence or absence of diseases by machine learning and supports doctors’ diagnosis, has been actively conducted. However, training of machine learning requires many training data with annotations. Since the annotations are done by radiologists manually, annotating hundreds to thousands of images is very hard work. This study proposes classifiers using convolutional neural network (CNN) with transfer learning for efficient opacity classification of diffuse lung diseases, and the effects of transfer learning are analyzed under various conditions. In detail, classifiers with nine different conditions of transfer learning and without transfer learning are compared to show the best conditions.