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Abstract：
Early and fast detection of disease is essential for the fight against COVID-19 pandemic. Researchers have focused on developing robust and cost-effective detection methods using Deep learning based chest X-Ray image processing. However, such prediction models are often not well suited to address the challenge of highly imabalanced datasets. The current work is an attempt to address the issue by utilizing unsupervised Variational Auto Encoders (VAEs). Firstly, chest X-Ray images are converted to a latent space by learning the most important features using VAEs. Secondly, a wide range of well established data resampling techniques are used to balance the preexisting imbalanced classes in the latent vector form of the dataset. Finally, the modified dataset in the new feature space is used to train well known classification models to classify chest X-Ray images into three different classes viz., \"COVID-19\", \"Pneumonia\", and \"Normal\". In order to capture the quality of resampling methods, 10-folds cross validation technique is applied on the dataset. Extensive experimental analysis have been carried out and results so obtained indicate significant improvement in COVID-19 detection using the proposed VAE based method. Furthermore, the ingenuity of the results have been established by performing Wilcoxon rank test with 95% level of significance.
摘要：
早期和快速检测疾病对于对抗COVID-19大流行至关重要。研究人员专注于使用基于深度学习的胸部X射线图像处理开发稳健且具有成本效益的检测方法。然而,这样的预测模型通常不适合解决高度一致的数据集的挑战。当前的工作是尝试通过利用无监督的变分自动编码器(VAE)来解决该问题。首先,通过使用VAE学习最重要的特征，将胸部X射线图像转换为潜在空间。其次,广泛的成熟的数据重采样技术被用来平衡数据集潜在向量形式中预先存在的不平衡类。最后,新特征空间中的修改后的数据集用于训练众所周知的分类模型,以将胸部X射线图像分类为三个不同的类别即。,“COVID-19”，“肺炎”，和“正常”。为了捕获重采样方法的质量，对数据集应用10倍交叉验证技术。已经进行了广泛的实验分析，所获得的结果表明，使用所提出的基于VAE的方法，COVID-19检测得到了显着改善。此外，通过进行95%显著性水平的Wilcoxon秩检验,确定了结果的独创性.
