论文标题
使用深卷积神经网络（CNN）转移学习，用于使用胸部X射线检测肺炎
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
论文作者
论文摘要
肺炎是一种威胁生命的疾病，发生在细菌感染或病毒感染引起的肺部。如果在适当的时间不采取行动，可能是生命的，因此对肺炎的早期诊断至关重要。本文的目的是使用数字X射线图像自动检测细菌和病毒性肺炎。它提供了有关准确检测肺炎的进步的详细报告，然后介绍了作者采用的方法。四个不同的预训练的深卷积神经网络（CNN）-Alexnet，Resnet18，Densenet201和Squeezenet用于转移学习。 5247细菌，病毒和正常的胸部X射线图像接受了预处理技术和修改图像的培训，以基于转移学习的分类任务。在这项工作中，作者报告了三种分类方案：正常与肺炎，细菌与病毒性肺炎和正常，细菌和病毒性肺炎。正常和肺炎图像，细菌和病毒性肺炎图像以及正常，细菌和病毒性肺炎的分类精度分别为98％，95％和93.3％。在任何方案中，这都比文献中报道的准确性最高。因此，拟议的研究可用于放射科医生更快地诊断肺炎，并可以帮助对肺炎患者进行快速筛查。
Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.
