A Transfer Learning Method with Deep Convolutional Neural Network for Diffuse Lung Disease Classification
We introduce a deep convolutional neural network (DCNN) as feature extraction method in a computer aided diagnosis (CAD) system in order to support diagnosis of diffuse lung diseases (DLD) on high-resolution computed tomography (HRCT) images. DCNN is a kind of multi layer neural network which can automatically extract features expression from the input data, however, it requires large amount of training data. In the field of medical image analysis, the number of acquired data is sometimes insufficient to train the learning system. Overcoming the problem, we apply a kind of transfer learning method into the training of the DCNN. At first, we apply massive natural images, which we can easily collect, for the pre-training. After that, small number of the DLD HRCT image as the labeled data is applied for fine-tuning. We compare DCNNs with training of (i) DLD HRCT images only, (ii) natural images only, and (iii) DLD HRCT images + natural images, and show the result of the case (iii) would be better DCNN feature rather than those of others.
KeywordsIdiopathic Pulmonary Fibrosis Natural Image Transfer Learning Idiopathic Interstitial Pneumonia Diffuse Lung Disease
This work is partly supported by MEXT/JSPS KAKENHI Grant number 25330285, and 26120515.
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