论文标题
评估当代卷积神经网络体系结构，用于从胸部射线照相检测COVID-19
Evaluation of Contemporary Convolutional Neural Network Architectures for Detecting COVID-19 from Chest Radiographs
论文作者
论文摘要
解释胸部X光片，又称胸部X射线，图像是医疗专业人员使用的必要且至关重要的诊断工具，可检测和识别许多可能困扰患者的疾病。尽管图像本身包含大量有价值的信息，但它们的用处可能会受到对其解释的解释的限制，尤其是当审查放射科医生可能疲劳或何时或经验丰富的放射科医生时。使用深度学习模型来分析胸部X光片的研究产生了令人印象深刻的结果，在某些情况下，这些模型表现优于执业医生。在Covid-19的大流行中，研究人员探索并提出了使用上述深层模型来检测X光片的Covid-19感染，以此作为帮助减轻医疗资源压力的一种可能方法。在这项研究中，我们培训和评估了三个模型体系结构，提议在各种条件下，在各种条件下找到了问题，这些问题折磨了当代研究对该主题提出的令人印象深刻的模型表演，并提出了培训方法的方法，以培训模型，以产生更可靠的结果。代码，脚本，脚本，预先训练的模型，并在https：//nalbithub.com/nalbertib.com/nnalbertbert/berterbert/cov中获得了可靠的模型。
Interpreting chest radiograph, a.ka. chest x-ray, images is a necessary and crucial diagnostic tool used by medical professionals to detect and identify many diseases that may plague a patient. Although the images themselves contain a wealth of valuable information, their usefulness may be limited by how well they are interpreted, especially when the reviewing radiologist may be fatigued or when or an experienced radiologist is unavailable. Research in the use of deep learning models to analyze chest radiographs yielded impressive results where, in some instances, the models outperformed practicing radiologists. Amidst the COVID-19 pandemic, researchers have explored and proposed the use of said deep models to detect COVID-19 infections from radiographs as a possible way to help ease the strain on medical resources. In this study, we train and evaluate three model architectures, proposed for chest radiograph analysis, under varying conditions, find issues that discount the impressive model performances proposed by contemporary studies on this subject, and propose methodologies to train models that yield more reliable results.. Code, scripts, pre-trained models, and visualizations are available at https://github.com/nalbert/COVID-detection-from-radiographs.
