Background: Accurate and fast diagnosis of COVID-19 is very important to manage the medical conditions of affected persons. The task is challenging owing to shortage and ineffectiveness of clinical testing kits. However, the existing problems can be improved by employing computational intelligent techniques on radiological images like CT-Scans (Computed Tomography) of lungs. Extensive research has been reported using deep learning models to diagnose the severity of COVID-19 from CT images. This has undoubtedly minimized the manual involvement in abnormality identification but reported detection accuracy is limited. Methods: The present work proposes an expert model based on deep features and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier to diagnose novel coronavirus. In this proposed model, features are extracted from the fully connected layer of transfer learned MobileNetv2 followed by FKNN training. The hyperparameters of FKNN are fine-tuned using PF-BAT. Results: The experimental results on the benchmark COVID CT scan data reveal that the proposed algorithm attains a validation accuracy of 99.38% which is better than the existing state-of-the-art methods proposed in past. Conclusion: The proposed model will help in timely and accurate identification of the coronavirus at the various phases. Such kind of rapid diagnosis will assist clinicians to manage the healthcare condition of patients well and will help in speedy recovery from the diseases. Clinical and Translational Impact Statement — The proposed automated system can provide accurate and fast detection of COVID-19 signature from lung radiographs. Also, the usage of lighter MobileNetv2 architecture makes it practical for deployment in real-time.

中文翻译：

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使用深度特征和无参数 BAT 优化自动诊断 COVID-19

背景：准确快速地诊断 COVID-19 对于管理受影响人员的医疗状况非常重要。由于临床检测试剂盒的短缺和无效，这项任务具有挑战性。但是，可以通过对肺部 CT 扫描（计算机断层扫描）等放射图像采用计算智能技术来改善现有问题。已有大量研究报告使用深度学习模型从 CT 图像诊断 COVID-19 的严重程度。这无疑最大限度地减少了人工参与异常识别，但报告的检测准确性有限。方法：目前的工作提出了一种基于深度特征和无参数 BAT (PF-BAT) 优化的模糊 K 近邻 (PF-FKNN) 分类器的专家模型来诊断新型冠状病毒。在这个提出的模型中，特征是从迁移学习的 MobileNetv2 的全连接层中提取的，然后是 FKNN 训练。FKNN 的超参数使用 PF-BAT 进行微调。结果： 在基准 COVID CT 扫描数据上的实验结果表明，所提出的算法达到了 99.38% 的验证准确率，这优于过去提出的现有最先进方法。 结论：拟议的模型将有助于及时准确地识别各个阶段的冠状病毒。这种快速诊断将有助于临床医生很好地管理患者的健康状况，并有助于疾病的快速康复。临床和转化影响声明 — 提议的自动化系统可以从肺部 X 光片中准确、快速地检测 COVID-19 特征。此外，使用更轻的 MobileNetv2 架构使其实时部署变得可行。