Key techniques for classification of thorax diseases based on deep learning

Deep learning has recently been widely used in medical image analysis due to its ability to learn complex features in images. An increasing number of deep learning methods has been devoted to the classification of chest X-ray (CXR) images. Most of the existing deep learning methods are based on classical pretraining models trained by global chest X-ray images. In this paper, we classify 14 different thorax diseases based on the RE-DSENet model, which applies the image registration algorithm to the multi-atlas segmentation model. The experimental results on the ChestX-ray14 dataset show that the RE-DSENet's classify-cation precision of 14 thorax diseases is significantly improved compared with three existing classic algorithms, and the average AUC (area under the ROC curve) score is up to 0.857. RE-DSENet can also reduce the computations in the classification network and improve the training efficiency. In addition, the focus areas diagnosed by the RE-DSENet model are visualized in this paper, and the results further prove the effectiveness of the model.