Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray
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DOI：
10.3390/app10093233
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发表时间：
2020-05-01
影响因子：
2.7
通讯作者：
Kashem, Saad
中科院分区：
文献类型：
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作者：
Rahman, Tawsifur;Chowdhury, Muhammad E. H.;Kashem, Saad
肺炎是一种威胁生命的疾病，它发生在肺部，由细菌或病毒感染引起。如果不及时采取行动，可能危及生命，因此肺炎的早期诊断至关重要。该论文旨在使用数字X射线图像自动检测细菌和病毒性肺炎。它提供了一个详细的报告，在准确检测肺炎的进展，然后介绍了作者所采用的方法。四种不同的预训练深度卷积神经网络（CNN）：AlexNet，ResNet18，DenseNet201和SqueezeNet用于迁移学习。总共有5247张胸部X射线图像，包括细菌、病毒和正常胸部X射线图像，经过预处理和训练，用于基于迁移学习的分类任务。在这项研究中，作者报告了三种分类方案：正常与肺炎，细菌与病毒性肺炎，以及正常，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准确率分别为98%、95%和93.3%。这是最高的准确度，在任何计划，在文献中报道的准确度。因此，这项研究有助于放射科医生更快地诊断肺炎，并有助于肺炎患者的快速机场筛查。
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 at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in 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. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, 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, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.