Problem

A worldwide challenge is to provide medical resources required for COVID-19 detection. They must be effective tools for fast detection and diagnose of the virus using a large number of tests; besides, they should be low-cost developments. While a chest X-ray scan is a powerful candidate tool, if several tests are carried out, the images produced by the devices must be interpreted accurately and rapidly. COVID-19 induces longitudinal pulmonary parenchymal ground-glass and consolidates pulmonary opacity, in some cases with rounded morphology and peripheral lung distribution, which is very difficult to predict in an early stage.

Aim

In this paper, we aim to develop a robust model to extract high-level features of COVID-19 from chest X-ray (CXR) images to help in rapid diagnosis. In specific, this paper proposes an optimization model for COVID-19 diagnosis based on adaptive Fuzzy C-means (AFCM) and improved Slime Mould Algorithm (SMA) based on Lévy distribution, namely AFCM-LSMA.

Methods

The SMA optimizer is proposed to adapt weights in oscillation mode and to mimic the process of generating positive and negative feedback from the propagation wave to shape the optimum path for food connectivity. Lévy motion is used as a permutation to perform a local search and to adapt SMA optimizer (LSMA) by generating several solutions that are apart from current candidates. Furthermore, it permits the optimizer to escape from local minima, examine large search areas and reach optimal solutions in fewer iterations with high convergence speed. The FCM algorithm is used to segment pulmonary regions from CXR images and is adapted to reduce time and amount of computations using histogram of the image intensities during the clustering process.

Results

The performance of the proposed AFCM-LSMA has been validated on CXR images and compared with different conventional machine learning and deep learning techniques, meta-heuristics methods, and different chaotic maps. The accuracies achieved by the proposed model are around (ACC = 0.96, RMSE = 0.23, Prec. = 0.98, F1_score = 0.98, MCC = 0.79, and Kappa = 0.79).

Conclusion

The experimental findings indicate that the proposed new method outperforms all other methods, which will be beneficial to the clinical practitioner for the early identification of infected COVID-19 patients.

中文翻译：

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AFCM-LSMA：基于Lévy煤泥霉菌算法和自适应模糊C均值的新型智能模型，可从胸部X射线图像识别COVID-19感染

问题

全球面临的挑战是提供COVID-19检测所需的医疗资源。它们必须是使用大量测试快速检测和诊断病毒的有效工具；此外，它们应该是低成本的发展。尽管胸部X光扫描是一种功能强大的候选工具，但是如果进行了几次测试，则必须准确，快速地解释设备产生的图像。COVID-19诱发纵向肺实质磨玻璃并巩固肺不透明性，在某些情况下具有圆形形态和周围肺部分布，这在早期很难预测。

目标

在本文中，我们旨在开发一个鲁棒的模型，以从胸部X射线（CXR）图像中提取COVID-19的高级特征，以帮助快速诊断。具体而言，本文提出了一种基于自适应模糊C均值（AFCM）和基于Lévy分布的改进的Slime Mould算法（SMA）的COVID-19诊断的优化模型，即AFCM-LSMA。

方法

提出了SMA优化器，以使权重适应振荡模式，并模仿从传播波产生正反馈和负反馈的过程，从而形成食品连通性的最佳路径。Lévy运动被用作排列，以执行局部搜索并通过生成除当前候选对象之外的其他解决方案来适应SMA优化器（LSMA）。此外，它允许优化器摆脱局部最小值，检查较大的搜索区域，并以较高的收敛速度以较少的迭代次数获得最佳解决方案。FCM算法用于从CXR图像中分割肺区域，并适用于在聚类过程中使用图像强度的直方图减少时间和计算量。

结果

所提出的AFCM-LSMA的性能已在CXR图像上得到验证，并与不同的常规机器学习和深度学习技术，元启发式方法以及不同的混沌图进行了比较。所提出的模型获得的精度约为（ACC  = 0.96，RMSE  = 0.23，Prec。  = 0.98，F1_score  = 0.98，MCC  = 0.79和Kappa  = 0.79）。

结论

实验结果表明，所提出的新方法优于所有其他方法，这对临床医生早期识别感染的COVID-19患者将是有益的。