Abstract
Chest X-ray has become one of the most common ways in diagnostic radiology exams, and this technology assists expert radiologists with finding the patients at potential risk of cardiopathy and lung diseases. However, it is still a challenge for expert radiologists to assess thousands of cases in a short period so that deep learning methods are introduced to tackle this problem. Since the diseases have correlations with each other and have hierarchical features, the traditional classification scheme could not achieve a good performance. In order to extract the correlation features among the diseases, some GCN-based models are introduced to combine the features extracted from the images to make prediction. This scheme can work well with the high quality of image features, so backbone with high computation cost plays a vital role in this scheme. However, a fast prediction in diagnostic radiology is also needed especially in case of emergency or region with low computation facilities, so we proposed an efficient convolutional neural network with GCN, which is named SGGCN, to meet the need of efficient computation and considerable accuracy. SGGCN used SGNet-101 as backbone, which is built by ShuffleGhost Block (Huang et al., 2021) to extract features with a low computation cost. In order to make sufficient usage of the information in GCN, a new GCN architecture is designed to combine information from different layers together in GCNM module so that we can utilize various hierarchical features and meanwhile make the GCN scheme faster. The experiment on CheXPert datasets illustrated that SGGCN achieves a considerable performance. Compared with GCN and ResNet-101 (He et al., 2015) backbone (test AUC 0.8080, parameters 4.7M and FLOPs 16.0B), the SGGCN achieves 0.7831 (−3.08%) test AUC with parameters 1.2M (−73.73%) and FLOPs 3.1B (−80.82%), where GCN with MobileNet (Sandler and Howard, 2018) backbone achieves 0.7531 (−6.79%) test AUC with parameters 0.5M (−88.46%) and FLOPs 0.66B (−95.88%).
Highlights
A potential risk of cardiopathy and lung disease threatens millions of lives, and most of these diseases are preventable due to the chest X-ray (CXR) technology
Some algorithms like convolutional neural network (CNN) and Bayesian models are introduced to process and make diseases prediction by CXR images, and they really make a difference. They reduce the workload of expert radiologists with the high speed of computation and make it possible for expert radiologists to process a huge number of radiology samples
These algorithms can filter out some low-risk radiology samples with a considerably low-false-negative rate so that expert radiologists can more find out the samples with potential risk
Summary
A potential risk of cardiopathy and lung disease threatens millions of lives, and most of these diseases are preventable due to the chest X-ray (CXR) technology. Some algorithms like convolutional neural network (CNN) and Bayesian models are introduced to process and make diseases prediction by CXR images, and they really make a difference. Graph Convolution Network (GCN) [7] is introduced to learn the hierarchical features among the labels, and this kind of structure might be suitable for this chest disease recognition task. Works like MLGCN [8] designed a proper structure, utilized the hierarchical features of labels, and achieved a better performance, but most of them adopt a deep neural network like ResNet-101 as backbone to extract image features, which would suffer high cost of computation. Compared with light models which have wide usage of depthwise and elementwise convolution, SGNet-101 could reduce the FLOPs and parameters and maintain the image features more . With the SGNet-101 as backbone and new GCN architecture, a new model named SGGCN is proposed by us
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