  This study aims to automatically diagnose thoracic diseases depicted on the
chest x-ray (CXR) images using deep convolutional neural networks. The existing
methods generally used the entire CXR images for training purposes, but this
strategy may suffer from two drawbacks. First, potential misalignment or the
existence of irrelevant objects in the entire CXR images may cause unnecessary
noise and thus limit the network performance. Second, the relatively low image
resolution caused by the resizing operation, which is a common preprocessing
procedure for training neural networks, may lead to the loss of image details,
making it difficult to detect pathologies with small lesion regions. To address
these issues, we present a novel method termed as segmentation-based deep
fusion network (SDFN), which leverages the domain knowledge and the
higherresolution information of local lung regions. Specifically, the local
lung regions were identified and cropped by the Lung Region Generator (LRG).
Two CNN-based classification models were then used as feature extractors to
obtain the discriminative features of the entire CXR images and the cropped
lung region images. Lastly, the obtained features were fused by the feature
fusion module for disease classification. Evaluated by the NIH benchmark split
on the Chest X-ray 14 Dataset, our experimental result demonstrated that the
developed method achieved more accurate disease classification compared with
the available approaches via the receiver operating characteristic (ROC)
analyses. It was also found that the SDFN could localize the lesion regions
more precisely as compared to the traditional method.
