论文标题
使用胸部放射学图像的可解释和轻巧的模型用于COVID-19检测
Explainable and Lightweight Model for COVID-19 Detection Using Chest Radiology Images
论文作者
论文摘要
胸部X射线（CXR）和计算机断层扫描（CT）图像的深度学习（DL）分析在最近由于COVID-19的大流行而引起了很多关注。卷积神经网络（CNN）非常适合于对大量数据进行培训时的图像分析任务。与其他任何领域相比，用于医学图像分析开发的应用需要高灵敏度和精度。提出的大多数用于检测COVID-19的工具具有高灵敏度和回忆性，但在对看不见的数据集进行测试时未能概括和执行。这鼓励我们使用使用（梯度加权的类激活映射）Grad-CAM技术生成的类激活图来可视化模型的预测，从而开发CNN模型，分析和理解其性能。这项研究详细讨论了图像级别所提出的模型的成功和失败。该模型的性能与最先进的DL模型进行了比较，并显示出可比性。使用的数据和代码可在https://github.com/aleesuss/c19上找到。
Deep learning (DL) analysis of Chest X-ray (CXR) and Computed tomography (CT) images has garnered a lot of attention in recent times due to the COVID-19 pandemic. Convolutional Neural Networks (CNNs) are well suited for the image analysis tasks when trained on humongous amounts of data. Applications developed for medical image analysis require high sensitivity and precision compared to any other fields. Most of the tools proposed for detection of COVID-19 claims to have high sensitivity and recalls but have failed to generalize and perform when tested on unseen datasets. This encouraged us to develop a CNN model, analyze and understand the performance of it by visualizing the predictions of the model using class activation maps generated using (Gradient-weighted Class Activation Mapping) Grad-CAM technique. This study provides a detailed discussion of the success and failure of the proposed model at an image level. Performance of the model is compared with state-of-the-art DL models and shown to be comparable. The data and code used are available at https://github.com/aleesuss/c19.
