Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

中文翻译：

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PSSPNN：PatchShuffle 随机池神经网络通过多路数据增强实现可解释的 COVID-19 诊断


目的。 COVID-19 已在世界各地造成大量死亡人数。准确诊断对于早期治疗具有重要意义。方法。在本研究中，我们提出了一种新的 PSSPNN 模型，用于对 COVID-19、继发性肺结核、社区捕获性肺炎和健康受试者进行分类。 PSSPNN 需要五个改进：我们首先提出了 n-conv 随机池模块。其次，提出了一种新颖的随机池化神经网络。第三，PatchShuffle 被引入作为正则化术语。第四，使用了改进的多路数据增强。第五，利用 Grad-CAM 来解释我们的 AI 模型。结果。在测试集上使用随机种子进行的 10 次运行表明，我们的算法实现了 95.79% 的微平均 F1 分数。此外，我们的方法比九种最先进的方法更好。结论。所提出的 PSSPNN 将帮助放射科医生更快、更准确地对 COVID-19 病例进行诊断。