Reverse-Transcription Polymerase Chain Reaction (RT-PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID-19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x-ray images using a pretrained AlexNet model thereby adopting a transfer learning approach. The dataset used for the study was obtained in the form of optical Coherence Tomography and chest X-ray images made available by Kermany et al. (2018, https://doi.org/10.17632/rscbjbr9sj.3) with a total number of 5853 pneumonia (positive) and normal (negative) images. To evaluate the average efficiency of the model, the dataset was split into on 50:50, 60:40, 70:30, 80:20 and 90:10 for training and testing respectively. To evaluate the performance of the model, 10 K Cross-validation was carried out. The performance of the model using overall dataset was compared with the means of cross-validation and the currents state of arts. The classification model has shown high performance in terms of accuracy, sensitivity and specificity. 70:30 split performed better compare to other splits with accuracy of 98.73%, sensitivity of 98.59% and specificity of 99.84%.

中文翻译：

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用于诊断病毒性肺炎和 COVID-19 类似疾病的卷积神经网络


逆转录聚合酶链反应（RT-PCR）方法是目前检测人体样本中病毒株的金标准方法，但该技术非常昂贵、耗时且经常导致误诊。最近爆​​发的 COVID-19 促使科学家们探索其他选择，例如使用人工智能驱动的工具作为检测病毒性肺炎的替代方法或确认方法。在本文中，我们利用卷积神经网络 (CNN) 方法使用预训练的 AlexNet 模型来检测 X 射线图像中的病毒性肺炎，从而采用迁移学习方法。用于研究的数据集以光学相干断层扫描和胸部 X 射线图像的形式获得，由 Kermany 等人提供。 （2018，https://doi.org/10.17632/rscbjbr9sj.3）共有 5853 张肺炎（阳性）和正常（阴性）图像。为了评估模型的平均效率，将数据集分为 50:50、60:40、70:30、80:20 和 90:10 分别进行训练和测试。为了评估模型的性能，进行了 10 K 交叉验证。使用整个数据集的模型的性能与交叉验证的方法和当前的技术水平进行了比较。该分类模型在准确性、敏感性和特异性方面表现出较高的性能。 70:30 分割比其他分割表现更好，准确度为 98.73%，灵敏度为 98.59%，特异性为 99.84%。