Abstract

Background:

Coronavirus Disease 2019 (COVID-19) is contagious, producing respiratory tract infection, caused by a newly discovered coronavirus. Its death toll is too high, and early diagnosis is the main problem nowadays. Infected people show a variety of symptoms such as fatigue, fever, tastelessness, dry cough, etc. Some other symptoms may also be manifested by radiographic visual identification. Therefore, Chest X-Rays (CXR) play a key role in the diagnosis of COVID-19.

Methods:

In this study, we use Chest X-Rays images to develop a computer-aided diagnosis (CAD) of the disease. These images are used to train two deep networks, the Convolution Neural Network (CNN), and the Long Short-Term Memory Network (LSTM) which is an artificial Recurrent Neural Network (RNN). The proposed study involves three phases. First, the CNN model is trained on raw CXR images. Next, it is trained on pre-processed CXR images and finally enhanced CXR images are used for deep network CNN training. Geometric transformations, color transformations, image enhancement, and noise injection techniques are used for augmentation. From augmentation, we get 3,220 augmented CXRs as training datasets. In the final phase, CNN is used to extract the features of CXR imagery that are fed to the LSTM model. The performance of the four trained models is evaluated by the evaluation techniques of different models, including accuracy, specificity, sensitivity, false-positive rate, and receiver operating characteristic (ROC) curve.

Results:

We compare our results with other benchmark CNN models. Our proposed CNN-LSTM model gives superior accuracy (99.02%) than the other state-of-the-art models. Our method to get improved input, helped the CNN model to produce a very high true positive rate (TPR 1) and no false-negative result whereas false negative was a major problem while using Raw CXR images.

Conclusions:

We conclude after performing different experiments that some image pre-processing and augmentation, remarkably improves the results of CNN-based models. It will help a better early detection of the disease that will eventually reduce the mortality rate of COVID.

中文翻译：

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计算机辅助的 COVID-19 诊断以及使用增强型 CXR 的深度学习器的比较

 抽象的

 背景：

2019 年冠状病毒病 (COVID-19) 具有传染性，由新发现的冠状病毒引起，会引起呼吸道感染。其死亡人数太高，早期诊断是当今的主要问题。感染者表现出多种症状，如疲劳、发烧、味淡、干咳等。通过放射线目视识别也可能表现出一些其他症状。因此，胸部 X 光检查 (CXR) 在 COVID-19 的诊断中发挥着关键作用。

 方法：

在这项研究中，我们使用胸部 X 光图像来开发该疾病的计算机辅助诊断 (CAD)。这些图像用于训练两个深度网络：卷积神经网络 (CNN) 和长短期记忆网络 (LSTM)，这是一种人工循环神经网络 (RNN)。拟议的研究分为三个阶段。首先，CNN 模型在原始 CXR 图像上进行训练。接下来，它在预处理的 CXR 图像上进行训练，最后将增强的 CXR 图像用于深度网络 CNN 训练。几何变换、颜色变换、图像增强和噪声注入技术用于增强。通过增强，我们获得了 3,220 个增强的 CXR 作为训练数据集。在最后阶段，CNN 用于提取 CXR 图像的特征，并将其输入 LSTM 模型。通过不同模型的评估技术来评估四个训练模型的性能，包括准确性、特异性、敏感性、假阳性率和受试者工作特征（ROC）曲线。

 结果：

我们将我们的结果与其他基准 CNN 模型进行比较。我们提出的 CNN-LSTM 模型比其他最先进的模型具有更高的准确性 (99.02%)。我们获得改进输入的方法帮助 CNN 模型产生非常高的真阳性率 (TPR 1)，并且没有假阴性结果，而假阴性是使用原始 CXR 图像时的一个主要问题。

 结论：

在进行不同的实验后，我们得出结论，一些图像预处理和增强可以显着改善基于 CNN 的模型的结果。它将有助于更好地及早发现疾病，最终降低新冠病毒的死亡率。