论文标题
Covidlite：一个具有白平衡的深度可分离深度神经网络，可检测COVID-19
COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19
论文作者
论文摘要
背景和客观：目前，全世界面临着一种大流行病，新型冠状病毒也被称为Covid-19，该病毒在200多个国家中分布在约330万例活性病例和440万人死亡的国家中。由于病例数量的迅速增加和测试套件的供应有限，因此需要替代诊断方法的可用性，以便在早期阶段包含COVID-19病例的扩散并减少死亡人数。为了提供替代性诊断方法，我们提出了一种基于深神网络的诊断方法，可以轻松地与移动设备集成，以检测使用胸部X射线（CXR）图像检测Covid-19和病毒性肺炎。方法：在这项研究中，我们提出了一种名为Covidlite的方法，该方法是白平衡的组合，然后是对比度有限的自适应直方图均衡（Clahe）和深度可分离的可分离卷积神经网络（DSCNN）。在这种方法中，白平衡接下来用作图像预处理步骤，以增强CXR图像的可见性，并使用稀疏交叉熵训练的DSCNN用于具有较小参数的图像分类，并且尺寸较轻，即8.4 MB，而无需量化。结果：拟议的covidlite方法与没有预处理的Vanilla DSCNN相比，相比之下，其性能提高了。提出的方法获得了二进制分类的99.58％的较高精度，而多类分类和表现出的各种最新方法的96.43％。结论：我们提出的方法，Covidlite在各种绩效指标上取得了出色的结果。通过详细的模型解释，共vid岩可以帮助放射科医生从CXR图像中检测Covid-19患者，并可以大大减少诊断时间。
Background and Objective:Currently, the whole world is facing a pandemic disease, novel Coronavirus also known as COVID-19, which spread in more than 200 countries with around 3.3 million active cases and 4.4 lakh deaths approximately. Due to rapid increase in number of cases and limited supply of testing kits, availability of alternative diagnostic method is necessary for containing the spread of COVID-19 cases at an early stage and reducing the death count. For making available an alternative diagnostic method, we proposed a deep neural network based diagnostic method which can be easily integrated with mobile devices for detection of COVID-19 and viral pneumonia using Chest X-rays (CXR) images. Methods:In this study, we have proposed a method named COVIDLite, which is a combination of white balance followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and depth-wise separable convolutional neural network (DSCNN). In this method, white balance followed by CLAHE is used as an image preprocessing step for enhancing the visibility of CXR images and DSCNN trained using sparse cross entropy is used for image classification with lesser parameters and significantly lighter in size, i.e., 8.4 MB without quantization. Results:The proposed COVIDLite method resulted in improved performance in comparison to vanilla DSCNN with no pre-processing. The proposed method achieved higher accuracy of 99.58% for binary classification, whereas 96.43% for multiclass classification and out-performed various state-of-the-art methods. Conclusion:Our proposed method, COVIDLite achieved exceptional results on various performance metrics. With detailed model interpretations, COVIDLite can assist radiologists in detecting COVID-19 patients from CXR images and can reduce the diagnosis time significantly.
