COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data.
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data.
复制标题
DOI：
10.1109/access.2020.3016780
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Shukla N
中科院分区：
文献类型：
--
作者：
Horry MJ;Chakraborty S;Paul M;Ulhaq A;Pradhan B;Saha M;Shukla N
及早检测COVID-19可能有助于制定适当的治疗计划和疾病控制决策。在这项研究中，我们展示了如何使用深度学习模型的迁移学习来执行COVID-19检测，使用来自三种最常用的医学成像模式X射线，超声和CT扫描的图像。其目的是通过智能深度学习图像分类模型为压力过大的医疗专业人员提供第二双眼睛。我们通过对几种流行的卷积神经网络（CNN）模型的初步比较研究，确定了一种合适的CNN模型。然后，我们针对图像模态优化了所选的VGG 19模型，以展示如何将这些模型用于高度稀缺且具有挑战性的COVID-19数据集。我们强调了利用当前公开的COVID-19数据集开发有用的深度学习模型所面临的挑战（包括数据集的大小和质量），以及它如何对复杂模型的可训练性产生不利影响。我们还提出了一个图像预处理阶段，以创建一个值得信赖的图像数据集，用于开发和测试深度学习模型。新方法旨在减少图像中不必要的噪声，以便深度学习模型可以专注于检测具有特定特征的疾病。我们的研究结果表明，超声图像提供上级检测精度相比，X射线和CT扫描。实验结果强调，在有限的数据下，大多数更深层的网络很难很好地训练，并且在我们使用的三种成像模式下提供的一致性较差。选定的VGG 19模型，然后使用适当的参数进行广泛的调整，在所有三种肺部图像模式下都可以进行相当程度的COVID-19肺炎检测或正常检测，X射线的精度高达86%，超声为100%，CT扫描为84%。
Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.