Purpose:

Recently, the outbreak of the novel coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. In fighting against COVID-19, effective diagnosis of infected patient is critical for preventing the spread of diseases. Due to limited availability of test kits, the need for auxiliary diagnostic approach has increased. Recent research has shown radiography of COVID-19 patient, such as CT and X-ray, contains salient information about the COVID-19 virus and could be used as an alternative diagnosis method. Chest X-ray (CXR) due to its faster imaging time, wide availability, low cost, and portability gains much attention and becomes very promising. In order to reduce intra- and inter-observer variability, during radiological assessment, computer-aided diagnostic tools have been used in order to supplement medical decision making and subsequent management. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologist in the interpretation of the collected data.

Method:

In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images. CXR images are enhanced using a local phase-based image enhancement method. The enhanced images, together with the original CXR data, are used as an input to our proposed CNN architecture. Using ablation studies, we show the effectiveness of the enhanced images in improving the diagnostic accuracy. We provide quantitative evaluation on two datasets and qualitative results for visual inspection. Quantitative evaluation is performed on data consisting of 8851 normal (healthy), 6045 pneumonia, and 3323 COVID-19 CXR scans.

Results:

In Dataset-1, our model achieves 95.57% average accuracy for a three classes classification, 99% precision, recall, and F1-scores for COVID-19 cases. For Dataset-2, we have obtained 94.44% average accuracy, and 95% precision, recall, and F1-scores for detection of COVID-19.

Conclusions:

Our proposed multi-feature-guided CNN achieves improved results compared to single-feature CNN proving the importance of the local phase-based CXR image enhancement. Future work will involve further evaluation of the proposed method on a larger-size COVID-19 dataset as they become available.

中文翻译：

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使用卷积神经网络改进 COVID-19 诊断的胸部 X 射线图像相位特征

目的：

近期，2019年新型冠状病毒病（COVID-19）大流行的爆发严重危害了人类的健康和生命。在抗击 COVID-19 的过程中，有效诊断受感染患者对于预防疾病传播至关重要。由于检测试剂盒的可用性有限，对辅助诊断方法的需求有所增加。最近的研究表明，COVID-19 患者的 X 光片（如 CT 和 X 射线）包含有关 COVID-19 病毒的显着信息，可用作替代诊断方法。胸部 X 光片 (CXR) 由于其成像时间快、可用性广、成本低和便携性等优点备受关注，并变得非常有前景。为了减少观察者内部和观察者之间的变异性，在放射学评估期间，计算机辅助诊断工具已被用于补充医疗决策和后续管理。需要具有高精度和稳健性的计算方法来快速对患者进行分类并帮助放射科医生解释收集的数据。

方法：

在这项研究中，我们设计了一种新颖的多特征卷积神经网络 (CNN) 架构，用于从 CXR 图像中对 COVID-19 进行多类改进分类。CXR 图像使用基于局部相位的图像增强方法进行增强。增强后的图像与原始 CXR 数据一起用作我们提出的 CNN 架构的输入。使用消融研究，我们展示了增强图像在提高诊断准确性方面的有效性。我们提供对两个数据集的定量评估和视觉检查的定性结果。对包括 8851 例正常（健康）、6045 例肺炎和 3323 例 COVID-19 CXR 扫描的数据进行定量评估。

结果：

在 Dataset-1 中，我们的模型实现了 95.57% 的三类分类平均准确率、99% 的准确率、召回率和 COVID-19 案例的 F1 分数。对于 Dataset-2，我们获得了 94.44% 的平均准确率和 95% 的准确率、召回率和 F1 分数，用于检测 COVID-19。

结论：

与单特征 CNN 相比，我们提出的多特征引导 CNN 取得了改进的结果，证明了基于局部相位的 CXR 图像增强的重要性。未来的工作将涉及在更大尺寸的 COVID-19 数据集可用时对所提出的方法进行进一步评估。