Aim:

By October 6, 2020, Coronavirus disease 2019 (COVID-19) was diagnosed worldwide, reaching 3,355,7427 people and 1,037,862 deaths. Detection of COVID-19 and pneumonia by the chest X-ray images is of great significance to control the development of the epidemic situation. The current COVID-19 and pneumonia detection system may suffer from two shortcomings: the selection of hyperparameters in the models is not appropriate, and the generalization ability of the model is poor.

Method:

To solve the above problems, our team proposed an improved intelligent global optimization algorithm, which is based on the biogeography-based optimization to automatically optimize the hyperparameters value of the models according to different detection objectives. In the optimization progress, after selecting the immigration of suitable index vector and the emigration of suitable index vector, we proposed adding a comparison operation to compare the value of them. According to the different numerical relationships between them, the corresponding operations are performed to improve the migration operation of biogeography-based optimization. The improved algorithm (momentum factor biogeography-based optimization) can better perform the automatic optimization operation. In addition, our team also proposed two frameworks: biogeography convolutional neural network and momentum factor biogeography convolutional neural network. And two methods for detection COVID-19 based on the proposed frameworks.

Results:

Our method used three convolutional neural networks (LeNet-5, VGG-16, and ResNet-18) as the basic classification models for chest X-ray images detection of COVID-19, Normal, and Pneumonia. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 1.56%, 1.48%, and 0.73% after using biogeography-based optimization to optimize the hyperparameters of the models. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 2.87%, 6.31%, and 1.46% after using the momentum factor biogeography-based optimization to optimize the hyperparameters of the models.

Conclusion:

Under the same experimental conditions, the performance of the momentum factor biogeography-based optimization is superior to the biogeography-based optimization in optimizing the hyperparameters of the convolutional neural networks. Experimental results show that the momentum factor biogeography-based optimization can improve the detection performance of the state-of-the-art approaches in terms of overall accuracy. In future research, we will continue to use and improve other global optimization algorithms to enhance the application ability of deep learning in medical pathological image detection.

中文翻译：

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MFBCNNC：用于通过胸部 X 光图像检测 COVID-19 的动量因子生物地理学卷积神经网络

目的：

到 2020 年 10 月 6 日，全球诊断出 2019 年冠状病毒病 (COVID-19)，影响 3,355,7427 人和 1,037,862 人死亡。通过胸部X线影像检测COVID-19和肺炎对控制疫情发展具有重要意义。当前的 COVID-19 和肺炎检测系统可能存在两个缺点：模型中超参数的选择不合适，模型的泛化能力差。

方法：

针对上述问题，我们团队提出了一种改进的智能全局优化算法，该算法在基于生物地理学的优化基础上，根据不同的检测目标自动优化模型的超参数值。在优化过程中，在选择合适的索引向量的迁移和合适的索引向量的迁移后，我们建议增加一个比较操作来比较它们的值。根据它们之间不同的数值关系，进行相应的运算，改进基于生物地理学优化的迁移运算。改进的算法（基于动量因子生物地理学的优化）可以更好地执行自动优化操作。此外，我们团队还提出了两个框架：生物地理学卷积神经网络和动量因子生物地理学卷积神经网络。以及基于所提出框架的两种检测 COVID-19 的方法。

结果：

我们的方法使用三个卷积神经网络（LeNet-5、VGG-16 和 ResNet-18）作为 COVID-19、正常和肺炎的胸部 X 射线图像检测的基本分类模型。在使用基于生物地理学的优化方法优化模型的超参数后，LeNet-5、VGG-16 和 ResNet-18 的准确率分别提高了 1.56%、1.48% 和 0.73%。LeNet-5、VGG-16 和 ResNet-18 在使用基于动量因子生物地理学的优化方法优化模型的超参数后，准确率分别提高了 2.87%、6.31% 和 1.46%。

结论：

在相同的实验条件下，基于动量因子生物地理学的优化在优化卷积神经网络的超参数方面的性能优于基于生物地理学的优化。实验结果表明，基于动量因子生物地理学的优化可以在整体精度方面提高最先进方法的检测性能。在未来的研究中，我们将继续使用和改进其他全局优化算法，以增强深度学习在医学病理图像检测中的应用能力。